Tag Archives: news

AWS Weekly Roundup: G6e instances, Karpenter, Amazon Prime Day metrics, AWS Certifications update and more (August 19, 2024)

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-g6e-instances-karpenter-amazon-prime-day-metrics-aws-certifications-update-and-more-august-19-2024/

You know what I find more exciting than the Amazon Prime Day sale? Finding out how Amazon Web Services (AWS) makes it all happen. Every year, I wait eagerly for Jeff Barr’s annual post to read the chart-topping metrics. The scale never ceases to amaze me.

This year, Channy Yun and Jeff Barr bring us behind the scenes of how AWS powered Prime Day 2024 for record-breaking sales. I will let you read the post for full details, but one metric that blows my mind every year is that of Amazon Aurora. On Prime Day, 6,311 Amazon Aurora database instances processed more than 376 billion transactions, stored 2,978 terabytes of data, and transferred 913 terabytes of data.

Amazon Box with checkbox showing a record breaking prime day event powered by AWS

Other news I’m excited to share is that registration is open for two new AWS Certification exams. You can now register for the beta version of the AWS Certified AI Practitioner and AWS Certified Machine Learning Engineer – Associate. These certifications are for everyone—from line-of-business professionals to experienced machine learning (ML) engineers—and will help individuals prepare for in-demand artificial intelligence and machine learning (AI/ML) careers. You can prepare for your exam by following a four-step exam prep plan for AWS Certified AI Practitioner and AWS Certified Machine Learning Engineer – Associate.

Last week’s launches
Here are some launches that got my attention:

General availability of Amazon Elastic Compute Cloud (Amazon EC2) EC2 G6e instances – Powered by NVIDIA L40S Tensor Core GPUs, G6e instances can be used for a wide range of ML and spatial computing use cases. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio.

Release of Karpenter 1.0 – Karpenter is a flexible, efficient, and high-performance Kubernetes compute management solution. You can use Karpenter with Amazon Elastic Kubernetes Service (Amazon EKS) or any conformant Kubernetes cluster. To learn more, visit the Karpenter 1.0 launch post.

Drag-and-drop UI for Amazon SageMaker Pipelines – With this launch, you can now quickly create, execute, and monitor an end-to-end AI/ML workflow to train, fine-tune, evaluate, and deploy models without writing code. You can drag and drop various steps of the workflow and connect them together in the UI to compose an AI/ML workflow.

Split, move and modify Amazon EC2 On-Demand Capacity Reservations – With the new capabilities for managing Amazon EC2 On-Demand Capacity Reservations, you can split your Capacity Reservations, move capacity between Capacity Reservations, and modify your Capacity Reservation’s instance eligibility attribute. To learn more about these features, refer to Split off available capacity from an existing Capacity Reservation.

Document-level sync reports in Amazon Q Business – This new feature of Amazon Q Business provides you with a comprehensive document-level report including granular indexing status, metadata, and access control list (ACL) details for every document processed during a data source sync job. You have the visibility of the status of the documents Amazon Q Business attempted to crawl and index as well as the ability to troubleshoot why certain documents were not returned with the expected answers.

Landing zone version selection in AWS Control Tower – Starting with landing zone version 3.1 and above, you can update or reset in-place your landing zone on the current version, or upgrade to a version of your choice. To learn more, visit Select a landing zone version in the AWS Control Tower user guide.

Launch of AWS Support Official channel on AWS re:Post – You now have access to curated content for operating at scale on AWS, authored by AWS Support and AWS Managed Services (AMS) experts. In this new channel, you can find technical solutions for complex problems, operational best practices, and insights into AWS Support and AMS offerings. To learn more, visit the AWS Support Official channel on re:Post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Regional expansion of AWS Services
Here are some of the expansions of AWS services into new AWS Regions that happened this week:

Amazon VPC Lattice is now available in 7 additional Regions – Amazon VPC Lattice is now available in US West (N. California), Africa (Cape Town), Europe (Milan), Europe (Paris), Asia Pacific (Mumbai), Asia Pacific (Seoul), and South America (São Paulo). With this launch, Amazon VPC Lattice is now generally available in 18 AWS Regions.

Amazon Q in QuickSight is now available in 5 additional Regions –  Amazon Q in QuickSight is now generally available in Asia Pacific (Mumbai), Canada (Central), Europe (Ireland), Europe (London), and South America (São Paulo), in addition to the existing US East (N. Virginia), US West (Oregon), and Europe (Frankfurt) Regions.

AWS Wickr is now available in the Europe (Zurich) Region – AWS Wickr adds Europe (Zurich) to the US East (N. Virginia), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (London), Europe (Frankfurt), and Europe (Stockholm) Regions that it’s available in.

You can browse the full list of AWS Services available by Region.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2024 – Dive into the first-round session catalog. Explore all the different learning opportunities at AWS re:Invent this year and start building your agenda today. You’ll find sessions for all interests and learning styles.

AWS Summits – The 2024 AWS Summit season is starting to wrap up! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Jakarta (September 5), and Toronto (September 11).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Colombia (August 24), New York (August 28), Belfast (September 6), and Bay Area (September 13).

AWS GenAI Lofts – Meet AWS AI experts and attend talks, workshops, fireside chats, and Q&As with industry leaders. All lofts are free and are carefully curated to offer something for everyone to help you accelerate your journey with AI. There are lofts scheduled in San Francisco (August 14–September 27), São Paulo (September 2–November 20), London (September 30–October 25), Paris (October 8–November 25), and Seoul (November).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

–Prasad

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

ASRock Rack GNRD8-2L2T Intel Xeon 6 Granite Rapids Motherboard Shown

Post Syndicated from John Lee original https://www.servethehome.com/asrock-rack-gnrd8-2l2t-intel-xeon-6-granite-rapids-motherboard-shown/

We spotted the ASRock Rack GNRD8-2L2T. This motherboard will support the Intel Xeon 6 R1S CPUs with 136 PCIe Gen5/ CXL 2.0 lanes

The post ASRock Rack GNRD8-2L2T Intel Xeon 6 Granite Rapids Motherboard Shown appeared first on ServeTheHome.

How AWS powered Prime Day 2024 for record-breaking sales

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/how-aws-powered-prime-day-2024-for-record-breaking-sales/

The last Amazon Prime Day 2024 (July 17-18) was Amazon’s biggest Prime Day shopping event ever, with record sales and more items sold during the two-day event than any previous Prime Day event. Prime members shopped for millions of deals and saved billions across more than 35 categories globally.

I live in South Korea, but luckily I was staying in Seattle to attend the AWS Heroes Summit during Prime Day 2024. I signed up for a Prime membership and used Rufus, my new AI-powered conversational shopping assistant, to search for items quickly and easily. Prime members in the U.S. like me chose to consolidate their deliveries on millions of orders during Prime Day, saving an estimated 10 million trips. This consolidation results in lower carbon emissions on average.

We know from Jeff’s annual blog post that AWS runs the Amazon website and mobile app that makes these short-term, large scale global events feasible. (check out his 2016, 2017, 2019, 2020, 2021, 2022, and 2023 posts for a look back). Today I want to share top numbers from AWS that made my amazing shopping experience possible.

Prime Day 2024 – all the numbers
Here are some of the most interesting and/or mind-blowing metrics:

Amazon EC2 – Since many of Amazon.com services such as Rufus and Search use AWS artificial intelligence (AI) chips under the hood, Amazon deployed a cluster of over 80,000 Inferentia and Trainium chips for Prime Day. During Prime Day 2024, Amazon used over 250K AWS Graviton chips to power more than 5,800 distinct Amazon.com services (double that of 2023).

Amazon EBS – In support of Prime Day, Amazon provisioned 264 PiB of Amazon EBS storage in 2024, a 62 percent increase compared to 2023. When compared to the day before Prime Day 2024, Amazon.com performance on Amazon EBS jumped by 5.6 trillion read/write I/O operations during the event, or an increase of 64 percent compared to Prime Day 2023. Also, when compared to the day before Prime Day 2024, Amazon.com transferred an incremental 444 petabytes of data during the event, or an increase of 81 percent compared to Prime Day 2023.

Amazon Aurora – On Prime Day, 6,311 database instances running the PostgreSQL-compatible and MySQL-compatible editions of Amazon Aurora processed more than 376 billion transactions, stored 2,978 terabytes of data, and transferred 913 terabytes of data.

Amazon DynamoDB – DynamoDB powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of Prime Day, these sources made tens of trillions of calls to the DynamoDB API. DynamoDB maintained high availability while delivering single-digit millisecond responses and peaking at 146 million requests per second.

Amazon ElastiCache – ElastiCache served more than quadrillion requests on a single day with a peak of over 1 trillion requests per minute.

Amazon QuickSight – Over the course of Prime Day 2024, one Amazon QuickSight dashboard used by Prime Day teams saw 107K unique hits, 1300+ unique visitors, and delivered over 1.6M queries.

Amazon SageMaker – SageMaker processed more than 145B inference requests during Prime Day.

Amazon Simple Email Service (Amazon SES) – SES sent 30 percent more emails for Amazon.com during Prime Day 2024 vs 2023, delivering 99.23 percent of those emails to customers.

Amazon GuardDuty – During Prime Day 2024, Amazon GuardDuty monitored nearly 6 trillion log events per hour, a 31.9% increase from the previous year’s Prime Day.

AWS CloudTrail – CloudTrail processed over 976 billion events in support of Prime Day 2024.

Amazon CloudFront – CloudFront handled a peak load of over 500 million HTTP requests per minute, for a total of over 1.3 trillion HTTP requests during Prime Day 2024, a 30 percent increase in total requests compared to Prime Day 2023.

Prepare to Scale
As Jeff noted in every year, rigorous preparation is key to the success of Prime Day and our other large-scale events. For example, 733 AWS Fault Injection Service experiments were run to test resilience and ensure Amazon.com remains highly available on Prime Day.

If you are preparing for a similar business-critical events, product launches, and migrations, I strongly recommend that you take advantage of newly-branded AWS Countdown, a support program designed for your project lifecycle to assess operational readiness, identify and mitigate risks, and plan capacity, using proven playbooks developed by AWS experts. For example, with additional help from AWS Countdown, Legal Zoom successfully migrated 450 servers with minimal issues and continues to leverage AWS Countdown Premium to streamline and expedite the launch of SaaS applications.

We look forward to seeing what other records will be broken next year!

— Channy & Jeff;

AWS Weekly Roundup: Mithra, Amazon Titan Image Generator v2, AWS GenAI Lofts, and more (August 12, 2024)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-mithra-amazon-titan-image-generator-v2-aws-genai-lofts-and-more-august-12-2024/

When Dr. Swami Sivasubramanian, VP of AI and Data, was an intern at Amazon in 2005, Dr. Werner Vogels, CTO of Amazon, was his first manager. Nineteen years later, the two shared a stage at the VivaTech Conference to reflect on Amazon’s history of innovation—from pioneering the pay-as-you-go model with Amazon Web Services (AWS) to transforming customer experiences using “good old-fashioned AI”—as well as what really keeps them up at night in the age of generative artificial intelligence (generative AI).

Asked if competitors ever kept him up at night, Dr. Werner insisted that listening to customer needs—such as guardrails, security, and privacy—and building products based on those needs is what drives success at Amazon. Dr. Swami said he viewed Amazon SageMaker and Amazon Bedrock as prime examples of successful products that have emerged as a result of this customer-first approach. “If you end up chasing your competitors, you are going to end up building what they are building,” he added. “If you actually listen to your customers, you are actually going to lead the way in innovation.” To learn four more lessons on customer-obsessed innovation, visit our AWS Careers blog.

For example, for customer-obsessed security, we build and use Mithra, a powerful neural network model to detect and respond to cyber threats. It analyzes up to 200 trillion internet domain requests daily from the AWS global network, identifying an average of 182,000 new malicious domains with remarkable accuracy. Mithra is just one example of how AWS uses global scale, advanced artificial intelligence and machine learning (AI/ML) technology, and constant innovation to lead the way in cloud security, making the internet safer for everyone. To learn more, visit the blog post of Chief Information Security Officer at Amazon CJ Moses, How AWS tracks the cloud’s biggest security threats and helps shut them down.

Last week’s launches
Here are some launches that got my attention:

Amazon Titan Image Generator v2 in Amazon Bedrock – With the new Amazon Titan Image Generator v2 model, you can guide image creation using a text prompt and reference images, control the color palette of generated images, remove backgrounds, and customize the model to maintain brand style and subject consistency. To learn more, visit my blog post, Amazon Titan Image Generator v2 is now available in Amazon Bedrock.

Regional expansion of Anthropic’s Claude models in Amazon Bedrock – The Claude 3.5 Sonnet, Anthropic’s latest high-performance AI model, is now available in US West (Oregon), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Singapore) Regions in Amazon Bedrock. The Claude 3 Haiku, Anthropic’s compact and affordable AI model, is now available in Asia Pacific (Tokyo) and Asia Pacific (Singapore) Regions in Amazon Bedrock.

Private IPv6 addressing for VPCs and subnets – You can now address private IPv6 for VPCs and subnets with Amazon VPC IP Address Manager (IPAM). Within IPAM, you can configure private IPv6 addresses in a private scope, provision Unique Local IPv6 Unicast Addresses (ULA) and Global Unicast Addresses (GUA), and use them to create VPCs and subnets for private access. To learn more, visit see the Understanding IPv6 addressing on AWS and designing a scalable addressing plan and VPC documentation,

Up to 30 GiB/s of read throughput in Amazon EFS – We are increasing the read throughput to 30 GiB/s, extending simple, fully elastic, and provisioning-free experience of Amazon EFS to support throughput-intensive AI and ML workloads for model training, inference, financial analytics, and genomic data analysis.

Large language models (LLMs) in Amazon Redshift ML – You can use pre-trained publicly available LLMs in Amazon SageMaker JumpStart as part of Amazon Redshift ML. For example, you can use LLMs to summarize feedback, perform entity extraction, and conduct sentiment analysis on data in your Amazon Redshift table, so you can bring the power of generative AI to your data warehouse.

Data products in Amazon DataZone – You can create data products in Amazon DataZone, which enable the grouping of data assets into well-defined, self-contained packages tailored for specific business use cases. For example, a marketing analysis data product can bundle various data assets such as marketing campaign data, pipeline data, and customer data. To learn more, visit this AWS Big Data blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items that you might find interesting:

AWS Goodies by Jeff Barr – Want to discover more exciting news about AWS? Jeff Barr is always in catch-up mode, doing his best to share all of the interesting things that he finds or that are shared with him. You can find his goodies once a week. Follow his LinkedIn page.

AWS and Multicloud – You might have missed a great article about the existing capabilities AWS has and the continued enhancements we’ve made in multicloud environments. In the post, Jeff covers the AWS approach to multicloud, provides you with some real-world examples, and reviews some of the newest multicloud and hybrid capabilities found across the lineup of AWS services.

Code transformation in Amazon Q Developer – At Amazon, we asked a small team to use Amazon Q Developer Agent for code transformation to migrate more than 30,000 production applications from older Java versions to Java 17. By using Amazon Q Developer to automate these upgrades, the team saved over 4,500 developer years of effort compared to what it would have taken to do all of these upgrades manually and saved the company $260 million in annual savings by moving to the latest Java version.

Contributing to AWS CDK – AWS Cloud Development Kit (AWS CDK) is an open source software development framework to model and provision your cloud application resources using familiar programming languages. Contributing to AWS CDK not only helps you deepen your knowledge of AWS services but also allows you to give back to the community and improve a tool you rely on.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2024 – Dive into the first-round session catalog. Explore all the different learning opportunities at AWS re:Invent this year and start building your agenda today. You’ll find sessions for all interests and learning styles.

AWS Innovate Migrate, Modernize, Build – Learn about proven strategies and practical steps for effectively migrating workloads to the AWS Cloud, modernizing applications, and building cloud-native and AI-enabled solutions. Don’t miss this opportunity to learn with the experts and unlock the full potential of AWS. Register now for Asia Pacific, Korea, and Japan (September 26).

AWS Summits – The 2024 AWS Summit season is almost wrapping up! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: São Paulo (August 15), Jakarta (September 5), and Toronto (September 11).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: New Zealand (August 15), Colombia (August 24), New York (August 28), Belfast (September 6), and Bay Area (September 13).

AWS GenAI Lofts – Meet AWS AI experts and attend talks, workshops, fireside chats, and Q&As with industry leaders. All lofts are free and are carefully curated to offer something for everyone to help you accelerate your journey with AI. There are lofts scheduled in San Francisco (August 14–September 27), São Paulo (September 2–November 20), London (September 30–October 25), Paris (October 8–November 25), and Seoul (November).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Amazon Titan Image Generator v2 is now available in Amazon Bedrock

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-titan-image-generator-v2-is-now-available-in-amazon-bedrock/

Today, we are announcing the general availability of the Amazon Titan Image Generator v2 model with new capabilities in Amazon Bedrock. With Amazon Titan Image Generator v2, you can guide image creation using reference images, edit existing visuals, remove backgrounds, generate image variations, and securely customize the model to maintain brand style and subject consistency. This powerful tool streamlines workflows, boosts productivity, and brings creative visions to life.

Amazon Titan Image Generator v2 brings a number of new features in addition to all features of Amazon Titan Image Generator v1, including:

  • Image conditioning – Provide a reference image along with a text prompt, resulting in outputs that follow the layout and structure of the user-supplied reference.
  • Image guidance with color palette – Control precisely the color palette of generated images by providing a list of hex codes along with the text prompt.
  • Background removal – Automatically remove background from images containing multiple objects.
  • Subject consistency – Fine-tune the model to preserve a specific subject (for example, a particular dog, shoe, or handbag) in the generated images.

New features in Amazon Titan Image Generator v2
Before getting started, if you are new to using Amazon Titan models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. To access the latest Amazon Titan models from Amazon, request access separately for Amazon Titan Image Generator G1 v2.

Here are details of the Amazon Titan Image Generator v2 in Amazon Bedrock:

Image conditioning
You can use the image conditioning feature to shape your creations with precision and intention. By providing a reference image (that is, a conditioning image), you can instruct the model to focus on specific visual characteristics, such as edges, object outlines, and structural elements, or segmentation maps that define distinct regions and objects within the reference image.

We support two types of image conditioning: Canny edge and segmentation.

  • The Canny edge algorithm is used to extract the prominent edges within the reference image, creating a map that the Amazon Titan Image Generator can then use to guide the generation process. You can “draw” the foundations of your desired image, and the model will then fill in the details, textures, and final aesthetic based on your guidance.
  • Segmentation provides an even more granular level of control. By supplying the reference image, you can define specific areas or objects within the image and instruct the Amazon Titan Image Generator to generate content that aligns with those defined regions. You can precisely control the placement and rendering of characters, objects, and other key elements.

Here are generation examples that use image conditioning.

To use the image conditioning feature, you can use Amazon Bedrock API, AWS SDK, or AWS Command Line Interface (AWS CLI) and choose CANNY_EDGE or SEGMENTATION for controlMode of textToImageParams with your reference image.

	"taskType": "TEXT_IMAGE",
	"textToImageParams": {
 		"text": "a cartoon deer in a fairy world.",
        "conditionImage": input_image, # Optional
        "controlMode": "CANNY_EDGE" # Optional: CANNY_EDGE | SEGMENTATION
        "controlStrength": 0.7 # Optional: weight given to the condition image. Default: 0.7
     }

The following a Python code example using AWS SDK for Python (Boto3) shows how to invoke Amazon Titan Image Generator v2 on Amazon Bedrock to use image conditioning.

import base64
import io
import json
import logging
import boto3
from PIL import Image
from botocore.exceptions import ClientError

def main():
    """
    Entrypoint for Amazon Titan Image Generator V2 example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        model_id = 'amazon.titan-image-generator-v2:0'

        # Read image from file and encode it as base64 string.
        with open("/path/to/image", "rb") as image_file:
            input_image = base64.b64encode(image_file.read()).decode('utf8')

        body = json.dumps({
            "taskType": "TEXT_IMAGE",
            "textToImageParams": {
                "text": "a cartoon deer in a fairy world",
                "conditionImage": input_image,
                "controlMode": "CANNY_EDGE",
                "controlStrength": 0.7
            },
            "imageGenerationConfig": {
                "numberOfImages": 1,
                "height": 512,
                "width": 512,
                "cfgScale": 8.0
            }
        })

        image_bytes = generate_image(model_id=model_id,
                                     body=body)
        image = Image.open(io.BytesIO(image_bytes))
        image.show()

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating image with Amazon Titan Image Generator V2 model {model_id}.")

def generate_image(model_id, body):
    """
    Generate an image using Amazon Titan Image Generator V2 model on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        image_bytes (bytes): The image generated by the model.
    """

    logger.info(
        "Generating image with Amazon Titan Image Generator V2 model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    base64_image = response_body.get("images")[0]
    base64_bytes = base64_image.encode('ascii')
    image_bytes = base64.b64decode(base64_bytes)

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Image generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated image with Amazon Titan Image Generator V2 model %s", model_id)

    return image_bytes
	
class ImageError(Exception):
    "Custom exception for errors returned by Amazon Titan Image Generator V2"

    def __init__(self, message):
        self.message = message

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

if __name__ == "__main__":
    main()

Color conditioning
Most designers want to generate images adhering to color branding guidelines so they seek control over color palette in the generated images.

With the Amazon Titan Image Generator v2, you can generate color-conditioned images based on a color palette—a list of hex colors provided as part of the inputs adhering to color branding guidelines. You can also provide a reference image as input (optional) to generate an image with provided hex colors while inheriting style from the reference image.

In this example, the prompt describes:
a jar of salad dressing in a rustic kitchen surrounded by fresh vegetables with studio lighting

The generated image reflects both the content of the text prompt and the specified color scheme to align with the brand’s color guidelines.

To use color conditioning feature, you can set taskType to COLOR_GUIDED_GENERATION with your prompt and hex codes.

       "taskType": "COLOR_GUIDED_GENERATION",
       "colorGuidedGenerationParam": {
             "text": "a jar of salad dressing in a rustic kitchen surrounded by fresh vegetables with studio lighting",                         
	         "colors": ['#ff8080', '#ffb280', '#ffe680', '#e5ff80'], # Optional: list of color hex codes 
             "referenceImage": input_image, #Optional
        }

Background removal
Whether you’re looking to composite an image onto a solid color backdrop or layer it over another scene, the ability to cleanly and accurately remove the background is an essential tool in the creative workflow. You can instantly remove the background from your images with a single step. Amazon Titan Image Generator v2 can intelligently detect and segment multiple foreground objects, ensuring that even complex scenes with overlapping elements are cleanly isolated.

The example shows an image of an iguana sitting on a tree in a forest. The model was able to identify the iguana as the main object and remove the forest background, replacing it with a transparent background. This lets the iguana stand out clearly without the distracting forest around it.

To use background removal feature, you can set taskType to BACKGROUND_REMOVAL with your input image.

    "taskType": "BACKGROUND_REMOVAL",
    "backgroundRemovalParams": {
 		"image": input_image,
    }

Subject consistency with fine-tuning
You can now seamlessly incorporate specific subjects into visually captivating scenes. Whether it’s a brand’s product, a company logo, or a beloved family pet, you can fine-tune the Amazon Titan model using reference images to learn the unique characteristics of the chosen subject.

Once the model is fine-tuned, you can simply provide a text prompt, and the Amazon Titan Generator will generate images that maintain a consistent depiction of the subject, placing it naturally within diverse, imaginative contexts. This opens up a world of possibilities for marketing, advertising, and visual storytelling.

For example, you could use an image with the caption Ron the dog during fine-tuning, give the prompt as Ron the dog wearing a superhero cape during inference with the fine-tuned model, and get a unique image in response.

To learn, visit model inference parameters and code examples for Amazon Titan Image Generator in the AWS documentation.

Now available
The Amazon Titan Generator v2 model is available today in Amazon Bedrock in the US East (N. Virginia) and US West (Oregon) Regions. Check the full Region list for future updates. To learn more, check out the Amazon Titan product page and the Amazon Bedrock pricing page.

Give Amazon Titan Image Generator v2 a try in Amazon Bedrock today, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Visit our community.aws site to find deep-dive technical content and to discover how our Builder communities are using Amazon Bedrock in their solutions.

— Channy

How AI Platforms Are Being Used to Scout Future Athletes at the Olympics

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/how-ai-platforms-are-being-used-to-scout-future-athletes-at-the-olympics-intel/

We checked out the Intel AI Platform Experience at the Olympic Games to see how AI is helping scout global athletic talent

The post How AI Platforms Are Being Used to Scout Future Athletes at the Olympics appeared first on ServeTheHome.

AWS Weekly Roundup: Amazon Q Business, AWS CloudFormation, Amazon WorkSpaces update, and more (Aug 5, 2024)

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-q-business-aws-cloudformation-amazon-workspaces-update-and-more-aug-5-2024/

Summer is reaching its peak for some of us around the globe, and many are heading out to their favorite holiday destinations to enjoy some time off. I just came back from holidays myself and I couldn’t help thinking about the key role that artificial intelligence (AI) plays in our modern world to help us scale the operation of simple things like traveling. Passport and identity verifications were quick, and thanks to the new airport security system rolling out across the world, so were my bag checks. I watched my backpack with a smile as it rolled along the security check belt with my computer, tablet, and portable game consoles all nicely tucked inside without any fuss.

If it wasn’t for AI, we wouldn’t be able to scale operations to keep up with population growth or the enormous volumes of data we generate on a daily basis. The advent of generative AI took this even further by unlocking the ability to put all this data to use in all kinds of creative ways, driving a new wave of exciting innovations that continues to elevate modern products and services.

This new landscape can be challenging for companies that are learning how generative AI can help them grow or succeed, such as startups. This is why I’m so excited about the AWS GenAI Lofts taking place in the next months around the world.

The AWS GenAI Lofts are collaborative spaces available in different cities around the world for a number of weeks. Startups, developers, investors, and industry experts can meet here while having access to AWS AI experts, and attend talks, workshops, fireside chats, and Q&As with industry leaders. All lofts are free and are carefully curated to offer something for everyone to help you accelerate your journey with AI. There are lofts scheduled in Bengaluru (July 29-Aug 9), San Francisco (Aug 14-Sept 27), Sao Paulo (Sept 2-Nov 20), London (Sept 30-Oct 25), Paris (Oct 8-Nov 25), and Seoul (Nov, pending exact dates). I highly encourage you to have a look at the agendas of a loft near you and drop in to learn more about GenAI and connect with others.

Last week’s launches
Here are some launches that got my attention last week.

Amazon Q Business cross-Region IdC — Amazon Q Business is a generative AI-powered assistant that deeply understands your business by providing connectors that you can easily set up to unify data from various sources such as Amazon S3, Microsoft 365, and more. You can then generate content, answer questions, and even automate tasks that are relevant and specific to your business. Q Business integrates with AWS IAM Identity Center to ensure that data can only be accessed by those who are authorized to do so. Previously, the IAM Identity Center instance had to be located in the same Region as the Q Business application. Now, you can connect to one in a different Region.

Git sync status changes publish to Amazon EventBridge — AWS CloudFormation Git sync is a very handy feature that can help streamline your DevOps operations by allowing you to automatically update your AWS CloudFormation stacks whenever you commit changes to the template or deployment file in source control. As of last week, any sync status change is published in near real-time as an event to EventBridge. This enables you to take your GitOps workflow further and stay on top of your Git repositories or resource sync status changes.

Some AWS Pinpoint’s capabilities are now under AWS End User Messaging — AWS Pinpoint’s SMS, MMS, push, and text to voice capabilities have been shuffled and now are offered through their own service called AWS End User Messaging. There is no impact to existing applications and no changes to APIs, the AWS Command Line Interface (AWS CLI), or IAM policies, however, the new name is now reflected on the AWS Management Console, AWS Billing console dashboard, documentation, and other places.

Amazon WorkSpaces updates — Microsoft Visual Studio Professional and Microsoft Visual Studio Enterprise 2022 are now added to the list of available license included applications on Workspaces Personal. Additionally, Amazon WorkSpaces Thin Client has received Carbon Trust verification. As verified by the Carbon Trust, the total lifecycle carbon emission is 77kg CO2e and 50% of the product is made from recycled materials.

GenAI for the Public Sector — There has been two significant launches that may interest those in the public sector looking into getting started with generative AI. Amazon Bedrock is now a FedRAMP High authorized service in the AWS GovCloud (US-West) Region. Additionally, both Llama 3 8B and Lllama 3 70B are now also available in that Region making this a perfect opportunity to start experimenting with Bedrock and Llama 3 if you have workloads in the AWS GovCloud (US-West) Region.

Customers in Germany can now sign up for AWS using their bank account — That means no debit or credit card is needed to create AWS accounts if you have a billing address in Germany. This can help simplify payment of AWS invoices for some businesses, as well as make it easier for others to get started on AWS.

Learning Materials

These are my recommended learning materials for this week.

AWS Skill Builder — This is more of a broad recommendation, but I’m still surprised that so many people never heard of AWS Skill Builder or have not tried it yet. There is so much learning you can do for free including a lot of hands-on courses. In July alone, AWS Skill Builder has launched 25 new digital training products including AWS SimulLearn and AWS Cloud Quest: Generative AI which are game-based learning experiences. Speaking of that, did you know that if you need to renew your Cloud Practitioner certification you can do it simply by playing the AWS Cloud Quest: Recertify Cloud Practioner game?

Get started with agentic code interpreter — Earlier last month we released a new capability on Agents for Amazon Bedrock which allows agents to dynamically generate and execute code within a secure sandboxed environment. As usual, my colleague Mike Chambers has created a great video and blog post on community.aws showing how you can start using it today.

That’s it for this week. Check back next Monday for another Weekly Roundup!

Plan your advertising campaigns with Amazon Marketing Cloud on AWS Clean Rooms, now generally available

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/plan-your-advertising-campaigns-with-amazon-marketing-cloud-on-aws-clean-rooms-now-generally-available/

Today, we are announcing the general availability of Amazon Marketing Cloud (AMC) on AWS Clean Rooms to help advertisers use their first-party signals to collaborate with Amazon Ads unique signals. With this collaboration, advertisers can generate differentiated insights, discover new audiences, and enable advertising campaign planning, activation, and measurement use cases, all without having to move their underlying signals outside of their AWS account. With AMC on AWS Clean Rooms, customers can easily prepare their data, match and create audiences, use custom insights to activate more relevant advertising campaigns with Amazon Ads, and measure return on ad spend. All of this can be accomplished from the most secure cloud computing environment available today.

Advertisers continually strive to reach new audiences and deliver relevant, marketing campaigns to better engage their customers. Yet, the advertising and marketing landscape is undergoing a fundamental shift with signal loss and fragmentation. As such, advertisers and their partners need to collaborate together using signals that are stored across many applications to personalize their advertising campaigns. However, to work with one another to gather insights, companies typically need to share a copy of their signals with their partners, which is often not aligned with their data governance, security and privacy, IT, and legal teams’ policies. As a result, many businesses miss opportunities to fully maximize the value of their first-party signals and improve planning, activation, and measurement outcomes for their campaigns.

AMC on AWS Clean Rooms makes it easier and scalable for advertisers to use their first-party signals with Amazon Ads, including collaborating across event-level signals and modeling unique audiences to help improve media planning, activation, and outcomes without having to move underlying signals outside their cloud environment.

AMC on AWS Clean Rooms prerequisites (environment setup)
To get started with AMC on AWS Clean Rooms, the advertiser needs an AWS account and a dataset that contains user population and event-level data stored in open data formats (CSV, Parquet, or Iceberg) in an Amazon Simple Storage Service (Amazon S3) bucket. The next step is to send an email to the Amazon Ads team to request the creation of an AMC instance. Once an instance has been created, the Amazon Ads team will create an AWS Clean Rooms collaboration and invite the advertiser to join the collaboration.

How it works
1. Join an AWS Clean Rooms collaboration and create an ID namespace.
2. Configure and associate tables to an AMC collaboration.
3. Run an ID mapping workflow to create and populate the ID mapping table.
4. Run a query in AMC.

Walkthrough

1. Join an AWS Clean Rooms collaboration and create an ID namespace.
The advertiser will accept the collaboration invite by creating a membership in their AWS account. Once in the collaboration, the advertiser will access the AWS Clean Rooms console and then select the AWS Entity Resolution ID namespace generated when the collaboration was created to start the process of using their data for matching and collaboration in AWS Clean Rooms. Next, specify the AWS Glue table and the associated schema mapping and choose the S3 bucket in the same AWS Region as the collaboration for temporarily storing your data while it processes. Lastly, the advertiser will provide permissions to read your data input from AWS Glue and write to Amazon S3 on their behalf.

In the AirportLink collaboration shown in the following screenshot, the advertiser (member AirportLink2) accepts a collaboration invite sent by member AirportLink1.


2. Configure and associate tables to an AMC collaboration.
After joining the collaboration, the advertiser will create configured tables on their purchase data, add custom analysis rule, and associate the configured table to the collaboration.



Within the collaboration, the advertiser will set up a collaboration analysis rule to control which party can receive the result of a query run on the associated table.


3. Run an ID mapping workflow to create and populate the ID mapping table.
Now that the ID namespace is associated with the collaboration, the Amazon Ads team will create an ID mapping table in the AWS Clean Rooms console. This step requires both the advertiser (source) and the Amazon Ads team (target) to associate their ID namespace resources to the collaboration. Amazon Ads will provide the methods of mapping and configuration, add the details for querying to name the ID mapping table, and provide permission for AWS Clean Rooms to execute and track the ID mapping workflow job on their behalf. Finally, the Amazon Ads team will select Create and Populate to start the mapping workflow and generate an ID mapping table that captures a common user cohort, who were matched on the rules provided in Step 2.

4. Run a query in AMC.
Advertisers can either use templates or write a SQL query to run for analysis and get query results for further insights. They can run the SQL query in the following ways:

  • Run a SQL query with AMC data and the advertiser’s data that return the results to the advertiser’s S3 bucket using aggregate analysis. An example query is “How many of the customers who are registered for my email list saw the ads I’m running on Amazon?”
  • Run a SQL query to create an audience on the advertiser’s data or overlap with AMC signals that returns results to the S3 bucket of Amazon Ads. An example query is to generate an audience to target in an ad campaign.
  • Run an AWS Clean Rooms ML lookalike modeling job where Amazon Ads contributes the configured model and the advertiser contributes a seed audience. The resulting segment (list of user ad IDs) is sent to Amazon Ads.


After running the query, the advertiser can create an audience using a rule-based audience or a similar audience by navigating to the Audience tab in AMC. The output of the audience query will be sent directly to Amazon Demand Side Platform (DSP). The following table shows the options available to you when creating the audience:

If you want to
 Then
Use pre-built audience templates Select Create with instructional query from the dropdown list
Create custom audience queries Select Create new query from the dropdown list

When creating a new query, the advertiser will configure various options such as name, description, and date adjustments. Additionally, the advertiser can choose from the two following audience types:

– Rule-based audience – Create audience-based on the audience query.
– Similar audience – Create machine learning (ML) based audiences based on the seed audience outputs from the audience query.

Now available
AMC on AWS Clean Rooms is available in in the US East (N. Virginia) Region. Be sure to check the full Region list for future updates. Learn more about AMC on AWS Clean Rooms in the AWS documentation.

Give it a try by emailing the Amazon Ads team to get started and send feedback to the AWS re:Post for AWS Clean Rooms or through your usual AWS Support contacts.

— Veliswa

AWS and Multicloud: Existing capabilities & continued enhancements

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-and-multicloud-existing-capabilities-continued-enhancements/

When I speak to large-scale AWS customers about their challenges and concerns, the conversation often turns to the topic of multicloud. Whether by intent or by accident, these customers sometimes choose to make use of services from more than one cloud provider, sometimes in conjunction with applications or services that are still hosted on-premises. In some cases they made early, bottom-up choices at the team and division level, choosing cloud offerings from multiple vendors in the absence of a top-down mandate. In others, they acquired or merged with another organization and discovered a similar multi-vendor situation.

Regardless of the path, these customers tell me that they want to simplify and centralize their oversight and management of this diverse portfolio of cloud and on-premises resources. It is sometimes the case that the “multi” situation is time-bound, with a plan in place to ultimately consolidate operations in one place. It is also sometimes the case that the customer plans to retain their diverse portfolio.

AWS and multicloud
Our goal with AWS is to make you successful no matter what architectural choices you have made. In this post I want to outline our approach, share some capabilities that our customers have been using over the years, and provide you with an update on some of the more recent service announcements and content that we have created to give you guidance that will help you to succeed.

Our approach is to extend existing AWS operational and management capabilities to work in multicloud and hybrid environments. Because we extend existing capabilities, your investment in training, development, scripting, and runbooks is preserved, and actually becomes even more worthwhile since it applies to your other (non-AWS) resources. For example, you can use the same service (AWS Systems Manager) to patch and update Amazon Elastic Compute Cloud (Amazon EC2) instances, servers running on-premises, and servers provided by other cloud providers. Similarly, you can use Amazon CloudWatch to monitor applications, compute resources, and other cloud resources in all of those environments. These are two examples of how we are putting our approach into practice for you.

The AWS Solutions for Hybrid and Multicloud page contains additional examples of our extension-based approach to adding new capabilities, along some success stories from customers who have put the capabilities to use including Phillips 66 and Deutsche Börse.

Whether you choose to operate entirely on AWS or in multicloud and hybrid environments, one of the primary reasons to adopt AWS is the broad choice of services we offer, enabling you to innovate, build, deploy, and monitor your workloads. Just as we recently launched free data transfer out to the internet (DTO) when you want to move outside of AWS, we are committed to helping you be successful regardless of your approach.

Now that I have explained our approach and highlighted some of the principal multicloud service offerings, let’s take a look at a few of the newest multicloud and hybrid capabilities.

Multicloud launches
Since the beginning of 2023 we have launched eighteen new multicloud capabilities to existing AWS services, including 15 for data & analytics, 1 for security, and 2 for identity. Many of these launches add to the existing multicloud capabilities of the respective services:

AWS DataSync – This service transfers data between storage services. In addition to existing support for Google Cloud Storage, Azure Files, and Azure Blob Storage, we added support for five additional cloud service providers and storage services including Oracle Cloud Storage and DigitalOcean Spaces (full list). To learn more about this service, read What is AWS DataSync. To get started, I create a source location:

AWS Glue – This data integration service helps you to discover, prepare, and integrate all of your data at any scale. You can use it to connect to more than 80 different data sources, including cloud databases and analytics services. In October 2023, we introduced additional new connectors that allow you to move data bidirectionally between Amazon Simple Storage Service (Amazon S3), and either Azure Blob Storage or Azure Data Lake Storage (full list). We also launched six database connectors for AWS Glue for Apache Spark, including Teradata, SAP HANA, Azure SQL, Azure Cosmos DB, Vertica, and MongoDB (full list). To learn more about AWS Glue, read What is AWS Glue. I create a visual job flow to get started:

Amazon Athena – This serverless analytics service lets you use interactive SQL queries to analyze petabyte-scale data where it lives (more than 25 external data sources, including other cloud data stores), without copying or transforming it. Last year we added a new data source connector that allows you to query data in Google Cloud Storage. To learn more about Amazon Athena, read What is Amazon Athena.

Amazon AppFlow – You can take advantage of data and analytics in Google BigQuery using a connector available in Amazon AppFlow. To get started with Amazon AppFlow I create a flow and configure a data source:

Amazon Security Lake – This service helps you to achieve a more complete, organization-wide view of your security posture. It centralizes security data from your AWS environments, SaaS providers, on-premises environments, and cloud sources (Azure and GCP) into a purpose-built data lake. It became generally available last year, and now supports collection and analysis of security data from sources that support the Open Cybersecurity Schema Framework (OCSF) standard—more than 80 sources (full list).

AWS Secrets Manager – This service centrally manages secrets such as database credentials and API keys. Secrets are securely encrypted and can be centrally audited, with support for replication to support disaster recovery and multi-region applications. Last year we announced that you can Use AWS Secrets Manager to store and manage secrets in on-premises or multicloud workloads. To learn more, read What is AWS Secrets Manager.

AWS Identity and Access Management (IAM) – AWS IAM Identity Center now supports automated user provisioning from Google Workspace. The integration helps administrators simplify AWS access management across multiple accounts while maintaining familiar Google Workspace experiences for end users as they sign in.

Amazon CloudWatch – This service lets you query, visualize, and alarm on metrics of all sorts: application, AWS, on-premises, and multicloud. At re:Invent 2023 we added even more support for consolidation of hybrid, multicloud, and on-premises metrics. This new feature allows you to select and configure connectors that pull data from Amazon Managed Service for Prometheus, generic Prometheus, Amazon OpenSearch Service, Amazon RDS for MySQL, Amazon RDS for PostgreSQL, CSV files stored in Amazon Simple Storage Service (Amazon S3), and Microsoft Azure Monitor.

Multicloud content and guidance
Now that you know about some of our latest multicloud launches, let’s take a look at some of the blog posts and other content that my colleagues have created.

First, some blog posts:

Next, some of the most popular multicloud videos from AWS re:Invent 2023:

And finally, be sure to bookmark the AWS Solutions for Hybrid and Multicloud page.

We’re here to help
If you are running in a multicloud environment and are ready to simplify and centralize, be sure to reach out to your AWS Account Manager (AM) or Technical Account Manager (TAM). Both will be happy to help!

— Jeff;

AWS Weekly Roundup: Llama 3.1, Mistral Large 2, AWS Step Functions, AWS Certifications update, and more (July 29, 2024)

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-llama-3-1-mistral-large-2-aws-step-functions-aws-certifications-update-and-more-july-29-2024/

I’m always amazed by the talent and passion of our Amazon Web Services (AWS) community members, especially in their efforts to increase diversity, equity, and inclusion in the tech community.

Last week, I had the honor of speaking at the AWS User Group Women Bay Area meetup, led by Natalie. This group is dedicated to empowering and connecting women, providing a supportive environment to explore cloud computing. In Latin America, we recently had the privilege of supporting 12 women-led AWS User Groups from 10 countries in organizing two regional AWSome Women Community Summits, reaching over 800 women builders. There’s still more work to be done, but initiatives like these highlight the power of community in fostering an inclusive and diverse tech environment.

Women-Led AWS Community Events

Now, let’s turn our attention to other exciting news in the AWS universe from last week.

Last week’s launches
Here are some launches that got my attention:

Meta Llama 3.1 models – The Llama 3.1 models are Meta’s most advanced and capable models to date. The Llama 3.1 models are a collection of 8B, 70B, and 405B parameter size models that demonstrate state-of-the-art performance on a wide range of industry benchmarks and offer new capabilities for your generative artificial intelligence (generative AI) applications. Llama 3.1 models are now available in Amazon Bedrock (see Announcing Llama 3.1 405B, 70B, and 8B models from Meta in Amazon Bedrock) and Amazon SageMaker JumpStart (see Llama 3.1 models are now available in Amazon SageMaker JumpStart).

My colleagues Tiffany and Mike explored Llama 3.1 in last week’s episode of the weekly Build On Generative AI live stream. You can watch the full episode here!

BuildOn Generative AI Llama 3.1 launch

Mistral Large 2 model – Mistral Large 2 is the newest version of Mistral Large, and according to Mistral AI, it offers significant improvements across multilingual capabilities, math, reasoning, coding, and much more. Mistral AI’s Mistral Large 2 foundation model (FM) is now available in Amazon Bedrock. See Mistral Large 2 is now available in Amazon Bedrock for all the details. You can find code examples in the Mistral-on-AWS repo and the Amazon Bedrock User Guide.

Faster auto scaling for generative AI models – This new capability in Amazon SageMaker inference can help you reduce the time it takes for your generative AI models to scale automatically. You can now use sub-minute metrics and significantly reduce overall scaling latency for generative AI models. With this enhancement, you can improve the responsiveness of your generative AI applications as demand fluctuates. For more details, check out Amazon SageMaker inference launches faster auto scaling for generative AI models.

AWS Step Functions now supports customer managed keys – AWS Step Functions now supports the use of customer managed keys with AWS Key Management Service (AWS KMS) to encrypt Step Functions state machine and activity resources. This new capability lets you encrypt your workflow definitions and execution data using your own encryption keys. Visit the AWS Step Functions documentation and the AWS KMS documentation to learn more.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items and posts that you might find interesting:

AWS Certification: Addition of new exam question types – If you are planning to take the AWS Certified AI Practitioner or AWS Certified Machine Learning Engineer – Associate exam anytime soon, check out AWS Certification: Addition of new exam question types. These exams will be the first to include three new question types: ordering, matching, and case study. The post shares insights about the new question types and offers information to help you prepare.

New ordering question type in AWS Certifications

Amazon’s exabyte-scale migration from Apache Spark to Ray on Amazon EC2 – The Business Data Technologies (BDT) team at Amazon Retail has just flipped the switch to start quietly moving management of some of their largest production business intelligence (BI) datasets from Apache Spark over to Ray to help reduce both data processing time and cost. They’ve also contributed a critical component of their work (The Flash Compactor) back to Ray’s open source DeltaCAT project. Find the full story at Amazon’s Exabyte-Scale Migration from Apache Spark to Ray on Amazon EC2.

Running compaction jobs with Ray on Amazon EC2

From community.aws
Here are my top three personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS SummitsAWS Summits – The 2024 AWS Summit season is almost wrapping up! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Mexico City (August 7), São Paulo (August 15), and Jakarta (September 5).

AWS Community DaysAWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: New Zealand (August 15), Colombia (August 24), New York (August 28), Belfast (September 6), and Bay Area (September 13).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Antje

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Announcing Llama 3.1 405B, 70B, and 8B models from Meta in Amazon Bedrock

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/announcing-llama-3-1-405b-70b-and-8b-models-from-meta-in-amazon-bedrock/

Today, we are announcing the availability of Llama 3.1 models in Amazon Bedrock. The Llama 3.1 models are Meta’s most advanced and capable models to date. The Llama 3.1 models are a collection of 8B, 70B, and 405B parameter size models that demonstrate state-of-the-art performance on a wide range of industry benchmarks and offer new capabilities for your generative artificial intelligence (generative AI) applications.

All Llama 3.1 models support a 128K context length (an increase of 120K tokens from Llama 3) that has 16 times the capacity of Llama 3 models and improved reasoning for multilingual dialogue use cases in eight languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

You can now use three new Llama 3.1 models from Meta in Amazon Bedrock to build, experiment, and responsibly scale your generative AI ideas:

  • Llama 3.1 405B (preview) is the world’s largest publicly available large language model (LLM) according to Meta. The model sets a new standard for AI and is ideal for enterprise-level applications and research and development (R&D). It is ideal for tasks like synthetic data generation where the outputs of the model can be used to improve smaller Llama models and model distillations to transfer knowledge to smaller models from the 405B model. This model excels at general knowledge, long-form text generation, multilingual translation, machine translation, coding, math, tool use, enhanced contextual understanding, and advanced reasoning and decision-making. To learn more, visit the AWS Machine Learning Blog about using Llama 3.1 405B to generate synthetic data for model distillation.
  • Llama 3.1 70B is ideal for content creation, conversational AI, language understanding, R&D, and enterprise applications. The model excels at text summarization and accuracy, text classification, sentiment analysis and nuance reasoning, language modeling, dialogue systems, code generation, and following instructions.
  • Llama 3.1 8B is best suited for limited computational power and resources. The model excels at text summarization, text classification, sentiment analysis, and language translation requiring low-latency inferencing.

Meta measured the performance of Llama 3.1 on over 150 benchmark datasets that span a wide range of languages and extensive human evaluations. As you can see in the following chart, Llama 3.1 outperforms Llama 3 in every major benchmarking category.

To learn more about Llama 3.1 features and capabilities, visit the Llama 3.1 Model Card from Meta and Llama models in the AWS documentation.

You can take advantage of Llama 3.1’s responsible AI capabilities, combined with the data governance and model evaluation features of Amazon Bedrock to build secure and reliable generative AI applications with confidence.

  • Guardrails for Amazon Bedrock – By creating multiple guardrails with different configurations tailored to specific use cases, you can use Guardrails to promote safe interactions between users and your generative AI applications by implementing safeguards customized to your use cases and responsible AI policies. With Guardrails for Amazon Bedrock, you can continually monitor and analyze user inputs and model responses that might violate customer-defined policies, detect hallucination in model responses that are not grounded in enterprise data or are irrelevant to the user’s query, and evaluate across different models including custom and third-party models. To get started, visit Create a guardrail in the AWS documentation.
  • Model evaluation on Amazon Bedrock – You can evaluate, compare, and select the best Llama models for your use case in just a few steps using either automatic evaluation or human evaluation. With model evaluation on Amazon Bedrock, you can choose automatic evaluation with predefined metrics such as accuracy, robustness, and toxicity. Alternatively, you can choose human evaluation workflows for subjective or custom metrics such as relevance, style, and alignment to brand voice. Model evaluation provides built-in curated datasets or you can bring in your own datasets. To get started, visit Get started with model evaluation in the AWS documentation.

To learn more about how to keep your data and applications secure and private in AWS, visit the Amazon Bedrock Security and Privacy page.

Getting started with Llama 3.1 models in Amazon Bedrock
If you are new to using Llama models from Meta, go to the Amazon Bedrock console and choose Model access on the bottom left pane. To access the latest Llama 3.1 models from Meta, request access separately for Llama 3.1 8B Instruct, Llama 3.1 70B Instruct, or Llama 3.1 405B Instruct.

To request to be considered for access to the preview of Llama 3.1 405B in Amazon Bedrock, contact your AWS account team or submit a support ticket via the AWS Management Console. When creating the support ticket, select Amazon Bedrock as the Service and Models as the Category.

To test the Llama 3.1 models in the Amazon Bedrock console, choose Text or Chat under Playgrounds in the left menu pane. Then choose Select model and select Meta as the category and Llama 3.1 8B Instruct, Llama 3.1 70B Instruct, or Llama 3.1 405B Instruct as the model.

In the following example I selected the Llama 3.1 405B Instruct model.

By choosing View API request, you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. You can use model IDs such as meta.llama3-1-8b-instruct-v1, meta.llama3-1-70b-instruct-v1 , or meta.llama3-1-405b-instruct-v1.

Here is a sample of the AWS CLI command:

aws bedrock-runtime invoke-model \
  --model-id meta.llama3-1-405b-instruct-v1:0 \
--body "{\"prompt\":\" [INST]You are a very intelligent bot with exceptional critical thinking[/INST] I went to the market and bought 10 apples. I gave 2 apples to your friend and 2 to the helper. I then went and bought 5 more apples and ate 1. How many apples did I remain with? Let's think step by step.\",\"max_gen_len\":512,\"temperature\":0.5,\"top_p\":0.9}" \
  --cli-binary-format raw-in-base64-out \
  --region us-east-1 \
  invoke-model-output.txt

You can use code examples for Llama models in Amazon Bedrock using AWS SDKs to build your applications using various programming languages. The following Python code examples show how to send a text message to Llama using the Amazon Bedrock Converse API for text generation.

import boto3
from botocore.exceptions import ClientError

# Create a Bedrock Runtime client in the AWS Region you want to use.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Llama 3 8b Instruct.
model_id = "meta.llama3-1-405b-instruct-v1:0"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

You can also use all Llama 3.1 models (8B, 70B, and 405B) in Amazon SageMaker JumpStart. You can discover and deploy Llama 3.1 models with a few clicks in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK. You can operate your models with SageMaker features such as SageMaker Pipelines, SageMaker Debugger, or container logs under your virtual private cloud (VPC) controls, which help provide data security.

The fine-tuning for Llama 3.1 models in Amazon Bedrock and Amazon SageMaker JumpStart will be coming soon. When you build fine-tuned models in SageMaker JumpStart, you will also be able to import your custom models into Amazon Bedrock. To learn more, visit Meta Llama 3.1 models are now available in Amazon SageMaker JumpStart on the AWS Machine Learning Blog.

For customers who want to deploy Llama 3.1 models on AWS through self-managed machine learning workflows for greater flexibility and control of underlying resources, AWS Trainium and AWS Inferentia-powered Amazon Elastic Compute Cloud (Amazon EC2) instances enable high performance, cost-effective deployment of Llama 3.1 models on AWS. To learn more, visit AWS AI chips deliver high performance and low cost for Meta Llama 3.1 models on AWS in the AWS Machine Learning Blog.

To celebrate this launch, Parkin Kent, Business Development Manager at Meta, talks about the power of the Meta and Amazon collaboration, highlighting how Meta and Amazon are working together to push the boundaries of what’s possible with generative AI.

Discover how businesses are leveraging Llama models in Amazon Bedrock to harness the power of generative AI. Nomura, a global financial services group spanning 30 countries and regions, is democratizing generative AI across its organization using Llama models in Amazon Bedrock.

Now available
Llama 3.1 8B and 70B models from Meta are generally available and Llama 450B model is preview today in Amazon Bedrock in the US West (Oregon) Region. To request to be considered for access to the preview of Llama 3.1 405B in Amazon Bedrock, contact your AWS account team or submit a support ticket. Check the full Region list for future updates. To learn more, check out the Llama in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give Llama 3.1 a try in the Amazon Bedrock console today, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Visit our community.aws site to find deep-dive technical content and to discover how our Builder communities are using Amazon Bedrock in their solutions. Let me know what you build with Llama 3.1 in Amazon Bedrock!

— Channy

AWS Weekly Roundup: Global AWS Heroes Summit, AWS Lambda, Amazon Redshift, and more (July 22, 2024)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-global-aws-heroes-summit-aws-lambda-amazon-redshift-and-more-july-22-2024/

Last week, AWS Heroes from around the world gathered to celebrate the 10th anniversary of the AWS Heroes program at Global AWS Heroes Summit. This program recognizes a select group of AWS experts worldwide who go above and beyond in sharing their knowledge and making an impact within developer communities.

Matt Garman, CEO of AWS and a long-time supporter of developer communities, made a special appearance for a Q&A session with the Heroes to listen to their feedback and respond to their questions.

Here’s an epic photo from the AWS Heroes Summit:

As Matt mentioned in his Linkedin post, “The developer community has been core to everything we have done since the beginning of AWS.” Thank you, Heroes, for all you do. Wishing you all a safe flight home.

Last week’s launches
Here are some launches that caught my attention last week:

Announcing the July 2024 updates to Amazon Corretto — The latest updates for the Corretto distribution of OpenJDK is now available. This includes security and critical updates for the Long-Term Supported (LTS) and Feature (FR) versions.

New open-source Advanced MYSQL ODBC Driver now available for Amazon Aurora and RDS — The new AWS ODBC Driver for MYSQL provides faster switchover and failover times, and authentication support for AWS Secrets Manager and AWS Identity and Access Management (IAM), making it a more efficient and secure option for connecting to Amazon RDS and Amazon Aurora MySQL-compatible edition databases.

Productionize Fine-tuned Foundation Models from SageMaker Canvas — Amazon SageMaker Canvas now allows you to deploy fine-tuned Foundation Models (FMs) to SageMaker real-time inference endpoints, making it easier to integrate generative AI capabilities into your applications outside the SageMaker Canvas workspace.

AWS Lambda now supports SnapStart for Java functions that use the ARM64 architecture — Lambda SnapStart for Java functions on ARM64 architecture delivers up to 10x faster function startup performance and up to 34% better price performance compared to x86, enabling the building of highly responsive and scalable Java applications using AWS Lambda.

Amazon QuickSight improves controls performance — Amazon QuickSight has improved the performance of controls, allowing readers to interact with them immediately without having to wait for all relevant controls to reload. This enhancement reduces the loading time experienced by readers.

Amazon OpenSearch Serverless levels up speed and efficiency with smart caching — The new smart caching feature for indexing in Amazon OpenSearch Serverless automatically fetches and manages data, leading to faster data retrieval, efficient storage usage, and cost savings.

Amazon Redshift Serverless with lower base capacity available in the Europe (London) Region — Amazon Redshift Serverless now allows you to start with a lower data warehouse base capacity of 8 Redshift Processing Units (RPUs) in the Europe (London) region, providing more flexibility and cost-effective options for small to large workloads.

AWS Lambda now supports Amazon MQ for ActiveMQ and RabbitMQ in five new regions — AWS Lambda now supports Amazon MQ for ActiveMQ and RabbitMQ in five new regions, enabling you to build serverless applications with Lambda functions that are invoked based on messages posted to Amazon MQ message brokers.

From community.aws
Here’s my top 5 personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for upcoming AWS events:

AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. To learn more about future AWS Summit events, visit the AWS Summit page. Register in your nearest city: AWS Summit Taipei (July 23–24), AWS Summit Mexico City (Aug. 7), and AWS Summit Sao Paulo (Aug. 15).

AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. Upcoming AWS Community Days are in Aotearoa (Aug. 15), Nigeria (Aug. 24), New York (Aug. 28), and Belfast (Sept. 6).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Donnie

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup: Advanced capabilities in Amazon Bedrock and Amazon Q, and more (July 15, 2024).

Post Syndicated from Abhishek Gupta original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-advanced-capabilities-in-amazon-bedrock-and-amazon-q-and-more-july-15-2024/

As expected, there were lots of exciting launches and updates announced during the AWS Summit New York. You can quickly scan the highlights in Top Announcements of the AWS Summit in New York, 2024.

NY-Summit-feat-img

My colleagues and fellow AWS News Blog writers Veliswa Boya and Sébastien Stormacq were at the AWS Community Day Cameroon last week. They were energized to meet amazing professionals, mentors, and students – all willing to learn and exchange thoughts about cloud technologies. You can access the video replay to feel the vibes or just watch some of the talks!

AWS Community Day Cameroon 2024

Last week’s launches
In addition to the launches at the New York Summit, here are a few others that got my attention.

Advanced RAG capabilities Knowledge Bases for Amazon Bedrock – These include custom chunking options to enable customers to write their own chunking code as a Lambda function; smart parsing to extract information from complex data such as tables; and query reformulation to break down queries into simpler sub-queries, retrieve relevant information for each, and combine the results into a final comprehensive answer.

Amazon Bedrock Prompt Management and Prompt Flows – This is a preview launch of Prompt Management that help developers and prompt engineers get the best responses from foundation models for their use cases; and Prompt Flows accelerates the creation, testing, and deployment of workflows through an intuitive visual builder.

Fine-tuning for Anthropic’s Claude 3 Haiku in Amazon Bedrock (preview) – By providing your own task-specific training dataset, you can fine tune and customize Claude 3 Haiku to boost model accuracy, quality, and consistency to further tailor generative AI for your business.

IDE workspace context awareness in Amazon Q Developer chat – Users can now add @workspace to their chat message in Q Developer to ask questions about the code in the project they currently have open in the IDE. Q Developer automatically ingests and indexes all code files, configurations, and project structure, giving the chat comprehensive context across your entire application within the IDE.

New features in Amazon Q Business –  The new personalization capabilities in Amazon Q Business are automatically enabled and will use your enterprise’s employee profile data to improve their user experience. You can now get answers from text content in scanned PDFs, and images embedded in PDF documents, without having to use OCR for preprocessing and text extraction.

Amazon EC2 R8g instances powered by AWS Graviton4 are now generally available – Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances.

Vector search for Amazon MemoryDB is now generally available – Vector search for MemoryDB enables real-time machine learning (ML) and generative AI applications. It can store millions of vectors with single-digit millisecond query and update latencies at the highest levels of throughput with >99% recall.

Introducing Valkey GLIDE, an open source client library for Valkey and Redis open source – Valkey is an open source key-value data store that supports a variety of workloads such as caching, and message queues. Valkey GLIDE is one of the official client libraries for Valkey and it supports all Valkey commands. GLIDE supports Valkey 7.2 and above, and Redis open source 6.2, 7.0, and 7.2.

Amazon OpenSearch Service enhancements – Amazon OpenSearch Serverless now supports workloads up to 30TB of data for time-series collections enabling more data-intensive use cases, and an innovative caching mechanism that automatically fetches and intelligently manages data, leading to faster data retrieval, efficient storage usage, and cost savings. Amazon OpenSearch Service has now added support for AI powered Natural Language Query Generation in OpenSearch Dashboards Log Explorer so you can get started quickly with log analysis without first having to be proficient in PPL.

Open source release of Secrets Manager Agent for AWS Secrets Manager – Secrets Manager Agent is a language agnostic local HTTP service that you can install and use in your compute environments to read secrets from Secrets Manager and cache them in memory, instead of making a network call to Secrets Manager.

Amazon S3 Express One Zone now supports logging of all events in AWS CloudTrail – This capability lets you get details on who made API calls to S3 Express One Zone and when API calls were made, thereby enhancing data visibility for governance, compliance, and operational auditing.

Amazon CloudFront announces managed cache policies for web applications – Previously, Amazon CloudFront customers had two options for managed cache policies, and had to create custom cache policies for all other cases. With the new managed cache policies, CloudFront caches content based on the Cache-Control headers returned by the origin, and defaults to not caching when the header is not returned.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services in additional Regions:

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Context window overflow: Breaking the barrier – This blog post dives into intricate workings of generative artificial intelligence (AI) models, and why is it crucial to understand and mitigate the limitations of CWO (context window overflow).

Using Agents for Amazon Bedrock to interactively generate infrastructure as code – This blog post explores how Agents for Amazon Bedrock can be used to generate customized, organization standards-compliant IaC scripts directly from uploaded architecture diagrams.

Automating model customization in Amazon Bedrock with AWS Step Functions workflow – This blog post covers orchestrating repeatable and automated workflows for customizing Amazon Bedrock models and how AWS Step Functions can help overcome key pain points in model customization.

AWS open source news and updates – My colleague Ricardo Sueiras writes about open source projects, tools, and events from the AWS Community; check out Ricardo’s page for the latest updates.

Upcoming AWS events
Check your calendars and sign up for upcoming AWS events:

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. To learn more about future AWS Summit events, visit the AWS Summit page. Register in your nearest city: Bogotá (July 18), Taipei (July 23–24), AWS Summit Mexico City (Aug. 7), and AWS Summit Sao Paulo (Aug. 15).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. Upcoming AWS Community Days are in Aotearoa (Aug. 15), Nigeria (Aug. 24), New York (Aug. 28), and Belfast (Sept. 6).

Browse all upcoming AWS led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Abhishek

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Vector search for Amazon MemoryDB is now generally available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/vector-search-for-amazon-memorydb-is-now-generally-available/

Today, we are announcing the general availability of vector search for Amazon MemoryDB, a new capability that you can use to store, index, retrieve, and search vectors to develop real-time machine learning (ML) and generative artificial intelligence (generative AI) applications with in-memory performance and multi-AZ durability.

With this launch, Amazon MemoryDB delivers the fastest vector search performance at the highest recall rates among popular vector databases on Amazon Web Services (AWS). You no longer have to make trade-offs around throughput, recall, and latency, which are traditionally in tension with one another.

You can now use one MemoryDB database to store your application data and millions of vectors with single-digit millisecond query and update response times at the highest levels of recall. This simplifies your generative AI application architecture while delivering peak performance and reducing licensing cost, operational burden, and time to deliver insights on your data.

With vector search for Amazon MemoryDB, you can use the existing MemoryDB API to implement generative AI use cases such as Retrieval Augmented Generation (RAG), anomaly (fraud) detection, document retrieval, and real-time recommendation engines. You can also generate vector embeddings using artificial intelligence and machine learning (AI/ML) services like Amazon Bedrock and Amazon SageMaker and store them within MemoryDB.

Which use cases would benefit most from vector search for MemoryDB?
You can use vector search for MemoryDB for the following specific use cases:

1. Real-time semantic search for retrieval-augmented generation (RAG)
You can use vector search to retrieve relevant passages from a large corpus of data to augment a large language model (LLM). This is done by taking your document corpus, chunking them into discrete buckets of texts, and generating vector embeddings for each chunk with embedding models such as the Amazon Titan Multimodal Embeddings G1 model, then loading these vector embeddings into Amazon MemoryDB.

With RAG and MemoryDB, you can build real-time generative AI applications to find similar products or content by representing items as vectors, or you can search documents by representing text documents as dense vectors that capture semantic meaning.

2. Low latency durable semantic caching
Semantic caching is a process to reduce computational costs by storing previous results from the foundation model (FM) in-memory. You can store prior inferenced answers alongside the vector representation of the question in MemoryDB and reuse them instead of inferencing another answer from the LLM.

If a user’s query is semantically similar based on a defined similarity score to a prior question, MemoryDB will return the answer to the prior question. This use case will allow your generative AI application to respond faster with lower costs from making a new request to the FM and provide a faster user experience for your customers.

3. Real-time anomaly (fraud) detection
You can use vector search for anomaly (fraud) detection to supplement your rule-based and batch ML processes by storing transactional data represented by vectors, alongside metadata representing whether those transactions were identified as fraudulent or valid.

The machine learning processes can detect users’ fraudulent transactions when the net new transactions have a high similarity to vectors representing fraudulent transactions. With vector search for MemoryDB, you can detect fraud by modeling fraudulent transactions based on your batch ML models, then loading normal and fraudulent transactions into MemoryDB to generate their vector representations through statistical decomposition techniques such as principal component analysis (PCA).

As inbound transactions flow through your front-end application, you can run a vector search against MemoryDB by generating the transaction’s vector representation through PCA, and if the transaction is highly similar to a past detected fraudulent transaction, you can reject the transaction within single-digit milliseconds to minimize the risk of fraud.

Getting started with vector search for Amazon MemoryDB
Look at how to implement a simple semantic search application using vector search for MemoryDB.

Step 1. Create a cluster to support vector search
You can create a MemoryDB cluster to enable vector search within the MemoryDB console. Choose Enable vector search in the Cluster settings when you create or update a cluster. Vector search is available for MemoryDB version 7.1 and a single shard configuration.

Step 2. Create vector embeddings using the Amazon Titan Embeddings model
You can use Amazon Titan Text Embeddings or other embedding models to create vector embeddings, which is available in Amazon Bedrock. You can load your PDF file, split the text into chunks, and get vector data using a single API with LangChain libraries integrated with AWS services.

import redis
import numpy as np
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import BedrockEmbeddings

# Load a PDF file and split document
loader = PyPDFLoader(file_path=pdf_path)
        pages = loader.load_and_split()
        text_splitter = RecursiveCharacterTextSplitter(
            separators=["\n\n", "\n", ".", " "],
            chunk_size=1000,
            chunk_overlap=200,
        )
        chunks = loader.load_and_split(text_splitter)

# Create MemoryDB vector store the chunks and embedding details
client = RedisCluster(
        host=' mycluster.memorydb.us-east-1.amazonaws.com',
        port=6379,
        ssl=True,
        ssl_cert_reqs="none",
        decode_responses=True,
    )

embedding =  BedrockEmbeddings (
           region_name="us-east-1",
 endpoint_url=" https://bedrock-runtime.us-east-1.amazonaws.com",
    )

#Save embedding and metadata using hset into your MemoryDB cluster
for id, dd in enumerate(chucks*):
     y = embeddings.embed_documents([dd])
     j = np.array(y, dtype=np.float32).tobytes()
     client.hset(f'oakDoc:{id}', mapping={'embed': j, 'text': chunks[id] } )

Once you generate the vector embeddings using the Amazon Titan Text Embeddings model, you can connect to your MemoryDB cluster and save these embeddings using the MemoryDB HSET command.

Step 3. Create a vector index
To query your vector data, create a vector index using theFT.CREATE command. Vector indexes are also constructed and maintained over a subset of the MemoryDB keyspace. Vectors can be saved in JSON or HASH data types, and any modifications to the vector data are automatically updated in a keyspace of the vector index.

from redis.commands.search.field import TextField, VectorField

index = client.ft(idx:testIndex).create_index([
        VectorField(
            "embed",
            "FLAT",
            {
                "TYPE": "FLOAT32",
                "DIM": 1536,
                "DISTANCE_METRIC": "COSINE",
            }
        ),
        TextField("text")
        ]
    )

In MemoryDB, you can use four types of fields: numbers fields, tag fields, text fields, and vector fields. Vector fields support K-nearest neighbor searching (KNN) of fixed-sized vectors using the flat search (FLAT) and hierarchical navigable small worlds (HNSW) algorithm. The feature supports various distance metrics, such as euclidean, cosine, and inner product. We will use the euclidean distance, a measure of the angle distance between two points in vector space. The smaller the euclidean distance, the closer the vectors are to each other.

Step 4. Search the vector space
You can use FT.SEARCH and FT.AGGREGATE commands to query your vector data. Each operator uses one field in the index to identify a subset of the keys in the index. You can query and find filtered results by the distance between a vector field in MemoryDB and a query vector based on some predefined threshold (RADIUS).

from redis.commands.search.query import Query

# Query vector data
query = (
    Query("@vector:[VECTOR_RANGE $radius $vec]=>{$YIELD_DISTANCE_AS: score}")
     .paging(0, 3)
     .sort_by("vector score")
     .return_fields("id", "score")     
     .dialect(2)
)

# Find all vectors within 0.8 of the query vector
query_params = {
    "radius": 0.8,
    "vec": np.random.rand(VECTOR_DIMENSIONS).astype(np.float32).tobytes()
}

results = client.ft(index).search(query, query_params).docs

For example, when using cosine similarity, the RADIUS value ranges from 0 to 1, where a value closer to 1 means finding vectors more similar to the search center.

Here is an example result to find all vectors within 0.8 of the query vector.

[Document {'id': 'doc:a', 'payload': None, 'score': '0.243115246296'},
 Document {'id': 'doc:c', 'payload': None, 'score': '0.24981123209'},
 Document {'id': 'doc:b', 'payload': None, 'score': '0.251443207264'}]

To learn more, you can look at a sample generative AI application using RAG with MemoryDB as a vector store.

What’s new at GA
At re:Invent 2023, we released vector search for MemoryDB in preview. Based on customers’ feedback, here are the new features and improvements now available:

  • VECTOR_RANGE to allow MemoryDB to operate as a low latency durable semantic cache, enabling cost optimization and performance improvements for your generative AI applications.
  • SCORE to better filter on similarity when conducting vector search.
  • Shared memory to not duplicate vectors in memory. Vectors are stored within the MemoryDB keyspace and pointers to the vectors are stored in the vector index.
  • Performance improvements at high filtering rates to power the most performance-intensive generative AI applications.

Now available
Vector search is available in all Regions that MemoryDB is currently available. Learn more about vector search for Amazon MemoryDB in the AWS documentation.

Give it a try in the MemoryDB console and send feedback to the AWS re:Post for Amazon MemoryDB or through your usual AWS Support contacts.

— Channy

Build enterprise-grade applications with natural language using AWS App Studio (preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/build-custom-business-applications-without-cloud-expertise-using-aws-app-studio-preview/

Organizations often struggle to solve their business problems in areas like claims processing, inventory tracking, and project approvals. Custom business applications could provide a solution to solve these problems and help an organization work more effectively but have historically required a professional development team to build and maintain. But often, development capacity is unavailable or too expensive, leaving businesses using inefficient tools and processes.

Today, we’re announcing a public preview of AWS App Studio. App Studio is a generative artificial intelligence (AI)-powered service that uses natural language to create enterprise-grade applications in minutes, without requiring software development skills.

Here’s a quick look at what App Studio can do. Once I’m signed in to App Studio, I select CREATE A NEW APP using the generative AI assistant. I describe that I need a project approval app. App Studio then generates an app for me, complete with a user interface, data models, and business logic. The entire app generation process is complete in minutes. 

Note: This animation above shows the flow at an accelerated speed for demonstration purposes.

While writing this post, I discovered that App Studio is useful for various technical professionals. IT project managers, data engineers, and enterprise architects can use it to create and manage secure business applications in minutes instead of days. App Studio helps organizations build end-to-end custom applications, and it has two main user roles:

  • Admin – Members in this group can manage groups and roles, create and edit connectors, and maintain visibility into other apps built within their organization. In addition to these permissions, admins can also build apps of their own. To enable and set up App Studio or to learn more about what you can do as an administrator, you can jump to the Getting started with AWS App Studio (preview) section.
  • Builder – Members of the builder group can create, build, and share applications. If you’re more interested in the journey of building applications, you can skip to the Using App Studio as a builder: Creating an application section.

Getting started with AWS App Studio
AWS App Studio integrates with AWS IAM Identity Center, making it easier for me to secure access with the flexibility to integrate with existing single sign-on (SSO) and integration with Lightweight Directory Access Protocol (LDAP). Also, App Studio manages the application deployments and operations, removing the time and effort required to operate applications. Now, I can spend more of my time adding features to an application and customizing it to user needs.

Before I can use App Studio to create my applications, I need to enable the service. Here is how an administrator would set up an App Studio instance.

First, I need to go to the App Studio management console and choose Get started.

As mentioned, App Studio integrates with IAM Identity Center and will automatically detect if you have an existing organization instance in IAM Identity Center. To learn more about the difference between an organization and an account instance on IDC, you can visit the Manage organization and account instances of IAM Identity Center page.

In this case, I don’t have any organization instance, so App Studio will guide me through creating an account instance in IAM Identity Center. Here, as an administrator, I select Create an account instance for me. 

In the next section, Create users and groups and add them to App Studio, I need to define both an admin and builder group. In this section, I add myself as the admin, and I’ll add users into the builder group later. 

The last part of the onboarding process is to review and check the tick box in the Acknowledgment section, then select Set up.

When the onboarding process is complete, I can see from the Account page that my App Studio is Active and ready to use. At this point, I have a unique App Studio instance URL that I can access.

This onboarding scenario illustrates how you can start without an instance preconfigured in IAM Identity Center . Learn more on the Creating and setting up an App Studio instance for the first time page to understand how to use your existing IAM Identity Center instance. 

Because App Studio created the AWS IAM Identity Center account instance for me, I received an email along with instructions to sign in to App Studio. Once I select the link, I’ll need to create a password for my account and define the multi-factor authentication (MFA) to improve the security posture of my account.

Then, I can sign in to App Studio.

Add additional users (optional)
App Studio uses AWS IAM Identity Center to manage users and groups. This means that if I need to invite additional users into my App Studio instance, I need to do that in IAM Identity Center. 

For example, here’s the list of my users. I can add more users by selecting Add user. Once I’ve finished adding users, they will receive an email with the instructions to activate their accounts.

If I need to create additional groups, I can do so by selecting Create group on the Groups page. The following screenshot shows groups I’ve defined for my account instance in IAM Identity Center.

Using AWS App Studio as an administrator
Now, I’m switching to the App Studio and signing in as an administrator. Here, I can see two main sections: Admin hub and Builder hub.

As an administrator, I can grant users access to App Studio by associating existing user groups with roles in the Roles section:

To map the group I created in my IAM Identity Center, I select Add group and select the Group identifier and Role. There are three roles I can configure: admin, builder, and app user. To understand the difference between each role, visit the Managing access and roles in App Studio page.

As an administrator, I can incorporate various data sources with App Studio using connectors. App Studio provides built-in connectors to integrate with AWS services such as Amazon Aurora, Amazon DynamoDB, and Amazon Simple Storage Service (Amazon S3). It also has a built-in connector for Salesforce and a generic API and OpenAPI connector to integrate with third-party services.

Furthermore, App Studio automatically created a managed DynamoDB connector for me to get started. I also have the flexibility to create additional connectors by selecting Create connector. 

On this page, I can create other connectors to AWS services. If I need other AWS services, I can select Other AWS services. To learn how to define your IAM role for your connectors, visit Connect App Studio to other services with connectors.

Using App Studio as a builder: Creating an application
As a builder, I can use the App Studio generative AI–powered low-code building environment to create secure applications. To start, I can describe the application that I need in natural language, such as “Build an application to review and process invoices.” Then, App Studio will generate the application, complete with the data models, business logic, and a multipage UI.

Here’s where the fun begins. It’s time for me to build apps in App Studio. On the Builder hub page, I select Create app.

I give it a name, and there are two options for me to build the app: Generate an app with AI or Start from scratch. I select Generate an app with AI. 

On the next page, I can start building the app by simply describing what I need in the text box. I also can choose sample prompts which are available on the right panel. 

Then, App Studio will prepare app requirements for me. I can improve my plan for the application by refining the prompt and reviewing the updated requirements. Once I’m happy with the results, I select Generate app, and App Studio will generate an application for me.

I found this to be a good experience for me when I started building apps with App Studio. The generative AI capability built into App Studio generated an app for me in minutes, compared to the hours or even days it would have taken me to get to the same point using other tools.

After a few minutes, my app is ready. I also see that App Studio prepares a quick tutorial for me to navigate around and understand different areas. 

There are three main areas in App Studio: Pages, Automations, and Data. I always like to start building my apps by defining the data models first, so let’s navigate to the Data section.

In the Data section, I can model my application data with the managed data store powered by DynamoDB or using the available data connectors. Because I chose to let AI generate this app, I have all the data entities defined for me. If I opted to do it manually, I would need to create entities representing the different data tables and field types for my application.

Once I’m happy with the data entities, I can build visual pages. In this area, I can create the UI for my users. I can add and arrange components like tables, forms, and buttons to create a tailored experience for my end users.

While I’m building the app, I can see the live preview by selecting Preview. This is useful for testing the layout and functionality of my application.

But the highlight for me in these three areas is the Automations. With the automations, I can define rules, workflows, and any actions that define or extend my application’s business logic. Because I chose to build this application with App Studio’s generative AI assistant, it automatically created and wired up multiple different automations needed for my application.

For example, every time a new project is submitted, it will trigger an action to create a project and send a notification email. 

I can also extend my business logic by invoking API callouts, AWS Lambda, or other AWS services. Besides creating the project, I’d also like to archive the project in a flat-file format into an S3 bucket. To do that, I also need to do some processing, and I happen to already have the functionality built in an existing Lambda function.

Here, I select Invoke Lambda, as shown in the previous screenshot. Then, I need to set the Connector, Function name, and the Function event payload to pass into my existing Lambda function.

Finally, after I’m happy with all the UI pages, data entities, and automations, I can publish it by selecting Publish. I have the flexibility to publish my app in a Testing or Production environment. This helps me to test my application before pushing it to production.

Join the preview
AWS App Studio is currently in preview, and you can access it in the US West (Oregon) AWS Region, but your applications can connect to your data in other AWS Regions.

Build secure, scalable, and performant custom business applications to modernize and streamline mission-critical tasks with AWS App Studio. Learn more about all the features and functionalities on the AWS App Studio documentation page, and join the conversation in the #aws-app-studio channel in the AWS Developers Slack workspace.

Happy building,

— Donnie

Amazon Q Apps, now generally available, enables users to build their own generative AI apps

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/amazon-q-apps-now-generally-available-enables-users-to-build-their-own-generative-ai-apps/

When we launched Amazon Q Business in April 2024, we also previewed Amazon Q Apps. Amazon Q Apps is a capability within Amazon Q Business for users to create generative artificial intelligence (generative AI)–powered apps based on the organization’s data. Users can build apps using natural language and securely publish them to the organization’s app library for everyone to use.

After collecting your feedback and suggestions during the preview, today we’re making Amazon Q Apps generally available. We’re also adding some new capabilities that were not available during the preview, such as API for Amazon Q Apps and the ability to specify data sources at the individual card level.

I’ll expand on the new features in a moment, but let’s first look into how to get started with Amazon Q Apps.

Transform conversations into reusable apps
Amazon Q Apps allows users to generate an app from their conversation with Amazon Q Business. Amazon Q Apps intelligently captures the context of conversation to generate an app tailored to specific needs. Let’s see it in action!

As I started writing this post, I thought of getting help from Amazon Q Business to generate a product overview of Amazon Q Apps. After all, Amazon Q Business is for boosting workforce productivity. So, I uploaded the product messaging documents to an Amazon Simple Storage Service (Amazon S3) bucket and added it as a data source using Amazon S3 connector for Amazon Q Business.

I start my conversation with the prompt:

I’m writing a launch post for Amazon Q Apps.
Here is a short description of the product: Employees can create lightweight, purpose-built Amazon Q Apps within their broader Amazon Q Business application environment.
Generate an overview of the product and list its key features.

Amazon Q Business Chat

After starting the conversation, I realize that creating a product overview given a product description would also be useful for others in the organization. I choose Create Amazon Q App to create a reusable and shareable app.

Amazon Q Business automatically generates a prompt to create an Amazon Q App and displays the prompt to me to verify and edit if need be:

Build an app that takes in a short text description of a product or service, and outputs an overview of that product/service and a list of its key features, utilizing data about the product/service known to Q.

Amazon Q Apps Creator

I choose Generate to continue the creation of the app. It creates a Product Overview Generator app with four cards—two input cards to get user inputs and two output cards that display the product overview and its key features.

Product Overview Generator App

I can adjust the layout of the app by resizing the cards and moving them around.

Also, the prompts for the individual text output cards are automatically generated so I can view and edit them. I choose the edit icon of the Product Overview card to see the prompt in the side panel.

In the side panel, I can also select the source for the text output card to generate the output using either large language model (LLM) knowledge or approved data sources. For the approved data sources, I can select one or more data sources that are configured for this Amazon Q Business application. I select the Marketing (Amazon S3) data source I had configured for creating this app.

Edit Text Output Card Prompt and select source

As you would notice, I generated a fully functional app from the conversation itself without having to make any changes to the base prompt or the individual text output card prompts.

I can now publish this app to the organization’s app library by choosing Publish. But before publishing the app, let’s look at another way to create Amazon Q apps.

Create generative AI apps using natural language
Instead of using conversation in Amazon Q Business as a starting point to create an app, I can choose Apps and use my own words to describe the app I want to create. Or I can try out the prompts from one of the preconfigured examples.

Amazon Q App

I can enter the prompt to fit the purpose and choose Generate to create the app.

Share apps with your team
Once you’re happy with both layouts and prompts, and are ready to share the app, you can publish the app to a centralized app library to give access to all users of this Amazon Q Business application environment.

Amazon Q Apps inherits the robust security and governance controls from Amazon Q Business, ensuring that data sources, user permissions, and guardrails are maintained. So, when other users run the app, they only see responses based on data they have access to in the underlying data sources.

For the Product Overview Generator app I created, I choose Publish. It displays the preview of the app and provides an option to select up to three labels. Labels help classify the apps by departments in the organization or any other categories. After selecting the labels, I choose Publish again on the preview popup.

Publish Amazon Q App

The app will instantly be available in the Amazon Q Apps library for others to use, copy, and build on top of. I choose Library to browse the Amazon Q Apps Library and find my Product Overview Generator app.

Amazon Q Apps Library

Customize apps in the app library for your specific needs
Amazon Q Apps allows users to quickly scale their individual or team productivity by customizing and tailoring shared apps to their specific needs. Instead of starting from scratch, users can review existing apps, use them as-is, or modify them and publish their own versions to the app library.

Let’s browse the app library and find an app to customize. I choose the label General to find apps in that category.

Document Editing Assistant App

I see a Document Editing Assistant app that reviews documents to correct grammatical mistakes. I would like to create a new version of the app to include a document summary too. Let’s see how we can do it.

I choose Open, and it opens the app with an option to Customize.

Open Document Editing Assistant App

I choose Customize, and it creates a copy of the app for me to modify it.

Customize App

I update the Title and Description of the app by choosing the edit icon of the app title.

I can see the original App Prompt that was used to generate this app. I can copy the prompt and use it as the starting point to create a similar app by updating it to include a description of the functionality that I would like to add and have Amazon Q Apps Creator take care of it. Or I can continue modifying this copy of the app.

There is an option to edit or delete existing cards. For example, I can edit the prompt of the Edited Document text output card by choosing the edit icon of the card.

Edit Text Output Card Prompt

To add more features, you can add more cards, such as a user input, text output, file upload, or preconfigured plugin by your administrator. The file upload card, for example, can be used to provide a file as another data source to refine or fine-tune the answers to your questions. The plugin card can be used, for example, to create a Jira ticket for any action item that needs to be performed as a follow-up.

I choose Text output to add a new card that will summarize the document. I enter the title as Document Summary and prompt as follows:

Summarize the key points in @Upload Document in a couple of sentences

Add Text Output Card

Now, I can publish this customized app as a new app and share it with everyone in the organization.

What did we add after the preview?

As I mentioned, we have used your feedback and suggestions during the preview period to add new capabilities. Here are the new features we have added:

Specify data sources at card level – As I have shown while creating the app, you can specify data sources you would like the output to be generated from. We added this feature to improve the accuracy of the responses.

Your Amazon Q business instance can have multiple data sources configured. However, to create an app, you might need only a subset of these data sources, based on the use case. So now you can choose specific data sources for each of the text output cards in your app. Or if your usecase requires, you can configure the text output cards to use LLM knowledge instead of using any data sources.

Amazon Q Apps API – You can now create and manage Amazon Q Apps programmatically with APIs for managing apps, app library and app sessions. This allows you to integrate all the functionalities of Amazon Q Apps into the tools and applications of your choice.

Things to know:

  • Regions – Amazon Q Apps is generally available today in the Regions where Amazon Q Business is available, which are the US East (N. Virginia) and US West (Oregon) Regions.
  • Pricing – Amazon Q Apps is available with the Amazon Business Pro subscription ($20 per user per month), which gives users access to all the features of Amazon Q Business.
  • Learning resources – To learn more, visit Amazon Q Apps in the Amazon Q Business User Guide.

–  Prasad