Showcasing SecOps Metrics That Matter

Post Syndicated from Rapid7 original https://blog.rapid7.com/2023/07/06/showcasing-secops-metrics-that-matter/

Showcasing SecOps Metrics That Matter

This year, new rules from the Security and Exchange Commission (SEC) about board-level expertise, risk management, and public disclosures will take effect. The European Union is updating its regulations, as well. To meet these new requirements, organizations will need to explain to shareholders exactly how they assess cyber risk, describe security policies, and prove a significant level of board oversight.

In this climate, security leaders will be expected to advise the C-suite on SecOps activities. As a security professional, this can be a challenge. It’s also an opportunity to shape the structure and execution of business and go-to-market decisions.

Our latest ebook, Presenting Upward: How to Showcase SecOps Metrics That Matter offers practical and actionable advice on how to present security metrics in a language execs understand.

About those metrics

Cybersecurity metrics are essential to understand where you’re succeeding and where you may need to make changes.

Some examples include:

Number and disposition of security incidents: You have no control of this, but it gives execs insight into the risk they face. There’s an attack every 39 seconds somewhere. What’s life like in your security operation?

Mean time-to-detection (MTTD): This metric gives insight into both efficacy of tools and coverage of data (is the detection coming from a reported incident vs. a tool, etc.).

Mean time-to-respond (MTTR): This also gives insight into your ability to respond and whether your tools and processes meet your threats and use cases.

Cost-per-incident: This gives you insight into efficiency of process, tooling, and also potential staffing shortcomings (like the number of people or specific skills).

There are many other metrics you may need to track to understand your cybersecurity readiness. Good metrics will differ for every organization, depending on your risks, needs, compliance requirements, desired business outcomes, security maturity, and more.

Stories + metrics = success

Generally speaking, executives don’t usually want to get too deep in the weeds. So, your ability to present metrics in a way they understand is critical to achieve cybersecurity goals.

Execs typically want answers to questions like:

  • What are our risks, and how are we addressing them?
  • How secure are we compared to similar organizations?
  • Are we budgeting the right amount for cybersecurity?
  • Where do we have opportunities for efficiencies or consolidation?
  • How are we addressing that thing in the news?

So, when presenting to execs it’s essential to put metrics into context. One way to do this is to craft a narrative that brings metrics to life. Stories often have more of an impact than facts and figures alone. This isn’t anecdotal; neuroscience has shown that when we are presented with a story, we understand the information more deeply, remember longer, and are more likely to factor what it taught us into future decisions.
For more tips on crafting an effective narrative, and much more, download Presenting Upward: How to Showcase SecOps Metrics That Matter now.

Things Might Look a Little Different Around Here: Technical Documentation Gets an Upgrade

Post Syndicated from Alison McClelland original https://www.backblaze.com/blog/things-might-look-a-little-different-around-here-technical-documentation-gets-an-upgrade/

A decorative image of a computer displaying the title Introducing the New Backblaze B2 Cloud Storage Documentation Portal.

When you’re working hard on an IT or development project, you need to be able to find instructions about the tools you’re using quickly. And, it helps if those instructions are easy to use, easy to understand, and easy to share. 

On the Technical Publications team, we spend a lot of time thinking about how to make our docs just that—easy. 

Today, the fruits of a lot of thinking and reorganizing and refining are paying off. The new Backblaze technical documentation portal is live.

Explore the Portal ➔ 

What’s New in the Tech Docs Portal?

The documentation portal has been completely overhauled to deliver on-demand content with a modern look and feel. Whether you’re a developer, web user, or someone who wants to understand how our products and services work, our portal is designed to be user-friendly, with a clean and intuitive interface that makes it easy to navigate and find the information you need.

Here are some highlights of what you can look forward to:

  • New and updated articles right on the landing page—so you’re always the first to know about important content changes.
  • A powerful search engine to help you find topics quickly.
  • A more logical navigation menu that organizes content into sections for easy browsing.
  • Information about all of the Backblaze B2 features and services in the About section.

You can get started using the Backblaze UI quickly to create application keys, create buckets, manage your files, and more. If you’re programmatically managing your data, we’ve included resources such as SDKs, developer quick-start guides, and step-by-step integration guides. 

Perhaps the most exciting enhancement is our API documentation. This resource provides endpoints, parameters, and responses for all three of our APIs: S3-Compatible, B2 Native, and Partner API.   

For Fun: A Brief History of Technical Documentation

As our team put our heads together to think about how to announce the new portal, we went down some internet rabbit holes on the history of technical documentation. Technical documentation was recognized as a profession around the start of World War II when technical documents became a necessity for military purposes. (Note: This was also the same era that a “computer” referred to a job for a person, meaning “one who computes”.) But the first technical content in the Western world can be traced back to 1650 B.C—the Rhind Papyrus describes some of the mathematical knowledge and methods of the Egyptians. And the title of first Technical Writer? That goes to none other than poet Geoffrey Chaucer of Canterbury Tales fame for his lesser-known work “A Treatise on the Astrolabe”—a tool that measures angles to calculate time and determine latitude.

A photograph of an astrolabe.
An astrolabe, or, as the Smithsonian calls it, “the original smartphone.” Image source.

After that history lesson, we ourselves waxed a bit poetic about the “old days” when we wrote long manuals in word processing software that were meant to be printed, compiled long indexes for user guides using desktop publishing tools, and wrote more XML code in structured authoring programs than actual content. These days we use what-you-see-is-what-you-get (WYSIWYG) editors in cloud-based content management systems which make producing content much easier and quicker—and none of us are dreaming in HTML anymore. 

<section><p>Or maybe we are.</p></section>

Overall, the history of documentation in the tech industry reflects the changing needs of users and the progression of technology. It evolved from technical manuals for experts to user-centric, accessible resources for audiences of all levels of technical proficiency.

The Future of Backblaze Technical Documentation Portal

In the coming months, you’ll see even more Backblaze B2 Cloud Storage content including many third-party integration guides. Backblaze Computer Backup documentation will also find a home here in this new portal so that you’ll have a one-stop-shop for all of your Backblaze technical and help documentation needs. 

We are committed to providing the best possible customer-focused documentation experience. Explore the portal to see how our documentation can make using Backblaze even easier!

The post Things Might Look a Little Different Around Here: Technical Documentation Gets an Upgrade appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Building Generative AI into Marketing Strategies: A Primer

Post Syndicated from nnatri original https://aws.amazon.com/blogs/messaging-and-targeting/building-generative-ai-into-marketing-strategies-a-primer/

Introduction

Artificial Intelligence has undoubtedly shaped many industries and is poised to be one of the most transformative technologies in the 21st century. Among these is the field of marketing where the application of generative AI promises to transform the landscape. This blog post explores how generative AI can revolutionize marketing strategies, offering innovative solutions and opportunities.

According to Harvard Business Review, marketing’s core activities, such as understanding customer needs, matching them to products and services, and persuading people to buy, can be dramatically enhanced by AI. A 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value. The ability to leverage AI can not only help automate and streamline processes but also deliver personalized, engaging content to customers. It enhances the ability of marketers to target the right audience, predict consumer behavior, and provide personalized customer experiences. AI allows marketers to process and interpret massive amounts of data, converting it into actionable insights and strategies, thereby redefining the way businesses interact with customers.

Generating content is just one part of the equation. AI-generated content, no matter how good, is useless if it does not arrive at the intended audience at the right point of time. Integrating the generated content into an automated marketing pipeline that not only understands the customer profile but also delivers a personalized experience at the right point of interaction is also crucial to getting the intended action from the customer.

Amazon Web Services (AWS) provides a robust platform for implementing generative AI in marketing strategies. AWS offers a range of AI and machine learning services that can be leveraged for various marketing use cases, from content creation to customer segmentation and personalized recommendations. Two services that are instrumental to delivering customer contents and can be easily integrated with other generative AI services are Amazon Pinpoint and Amazon Simple Email Service. By integrating generative AI with Amazon Pinpoint and Amazon SES, marketers can automate the creation of personalized messages for their customers, enhancing the effectiveness of their campaigns. This combination allows for a seamless blend of AI-powered content generation and targeted, data-driven customer engagement.

As we delve deeper into this blog post, we’ll explore the mechanics of generative AI, its benefits and how AWS services can facilitate its integration into marketing communications.

What is Generative AI?

Generative AI is a subset of artificial intelligence that leverages machine learning techniques to generate new data instances that resemble your training data. It works by learning the underlying patterns and structures of the input data, and then uses this understanding to generate new, similar data. This is achieved through the use of models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.

What do Generative AI buzzwords mean?

In the world of AI, buzzwords are abundant. Terms like “deep learning”, “neural networks”, “machine learning”, “generative AI”, and “large language models” are often used interchangeably, but they each have distinct meanings. Understanding these terms is crucial for appreciating the capabilities and limitations of different AI technologies.

Machine Learning (ML) is a subset of AI that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. These algorithms can be ‘trained’ on a dataset and then used to predict or classify new data. Machine learning models can be broadly categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep Learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model and understand complex patterns. These layers of neurons process different features, and their outputs are combined to produce a final result. Deep learning models can handle large amounts of data and are particularly good at processing images, speech, and text.

Generative AI refers specifically to AI models that can generate new data that mimic the data they were trained on. This is achieved through the use of models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Generative AI can create anything from written content to visual designs, and even music, making it a versatile tool in the hands of marketers.

Large Language Models (LLMs) are a type of generative AI that are trained on a large corpus of text data and can generate human-like text. They predict the probability of a word given the previous words used in the text. They are particularly useful in applications like text completion, translation, summarization, and more. While they are a type of generative AI, they are specifically designed for handling text data.

Simply put, you can understand that Large Language Model is a subset of Generative AI, which is then a subset of Machine Learning and they ultimately falls under the umbrella term of Artificial Intelligence.

What are the problems with generative AI and marketing?

While generative AI holds immense potential for transforming marketing strategies, it’s important to be aware of its limitations and potential pitfalls, especially when it comes to content generation and customer engagement. Here are some common challenges that marketers should be aware of:

Bias in Generative AI Generative AI models learn from the data they are trained on. If the training data is biased, the AI model will likely reproduce these biases in its output. For example, if a model is trained primarily on data from one demographic, it may not accurately represent other demographics, leading to marketing campaigns that are ineffective or offensive. Imagine if you are trying to generate an image for a campaign targeting females, a generative AI model might not generate images of females in jobs like doctors, lawyers or judges, leading your campaign to suffer from bias and uninclusiveness.

Insensitivity to Cultural Nuances Generative AI models may not fully understand cultural nuances or sensitive topics, which can lead to content that is insensitive or even harmful. For instance, a generative AI model used to create social media posts for a global brand may inadvertently generate content that is seen as disrespectful or offensive by certain cultures or communities.

Potential for Inappropriate or Offensive Content Generative AI models can sometimes generate content that is inappropriate or offensive. This is often because the models do not fully understand the context in which certain words or phrases should be used. It’s important to have safeguards in place to review and approve content before it’s published. A common problem with LLMs is hallucination: whereby the model speaks false knowledge as if it is accurate. A marketing team might mistakenly publish a auto-generated promotional content that contains a 20% discount on an item when no such promotions were approved. This could have disastrous effect if safeguards are not in place and erodes customers’ trust.

Intellectual Property and Legal Concerns Generative AI models can create new content, such as images, music, videos, and text, which raises questions of ownership and potential copyright infringement. Being a relatively new field, legal discussions are still ongoing to discuss legal implications of using Generative AI, e.g. who should own generated AI content, and copyright infringement.

Not a Replacement for Human Creativity Finally, while generative AI can automate certain aspects of marketing campaigns, it cannot replace the creativity or emotional connections that marketers use in crafting compelling campaigns. The most successful marketing campaigns touch the hearts of the customers, and while Generative AI is very capable of replicating human content, it still lacks in mimicking that “human touch”.

In conclusion, while generative AI offers exciting possibilities for marketing, it’s important to approach its use with a clear understanding of its limitations and potential pitfalls. By doing so, marketers can leverage the benefits of generative AI while mitigating risks.

How can I use generative AI in marketing communications?

Amazon Web Services (AWS) provides a comprehensive suite of services that facilitate the use of generative AI in marketing. These services are designed to handle a variety of tasks, from data processing and storage to machine learning and analytics, making it easier for marketers to implement and benefit from generative AI technologies.

Overview of Relevant AWS Services

AWS offers several services that are particularly relevant for generative AI in marketing:

  • Amazon Bedrock: This service makes FMs accessible via an API. Bedrock offers the ability to access a range of powerful FMs for text and images, including Amazon’s Titan FMs. With Bedrock’s serverless experience, customers can easily find the right model for what they’re trying to get done, get started quickly, privately customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS tools and capabilities they are familiar with.
  • Amazon Titan Models: These are two new large language models (LLMs) that AWS is announcing. The first is a generative LLM for tasks such as summarization, text generation, classification, open-ended Q&A, and information extraction. The second is an embeddings LLM that translates text inputs into numerical representations (known as embeddings) that contain the semantic meaning of the text. In response to the pitfalls mentioned above around Generative AI hallucinations and inaccurate information, AWS is actively working on improving accuracy and ensuring its Titan models produce high-quality responses, said Bratin Saha, an AWS vice president.
  • Amazon SageMaker: This fully managed service enables data scientists and developers to build, train, and deploy machine learning models quickly. SageMaker includes modules that can be used for generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
  • Amazon Pinpoint: This flexible and scalable outbound and inbound marketing communications service enables businesses to engage with customers across multiple messaging channels. Amazon Pinpoint is designed to scale with your business, allowing you to send messages to a large number of users in a short amount of time. It integrates with AWS’s generative AI services to enable personalized, AI-driven marketing campaigns.
  • Amazon Simple Email Service (SES): This cost-effective, flexible, and scalable email service enables marketers to send transactional emails, marketing messages, and other types of high-quality content to their customers. SES integrates with other AWS services, making it easy to send emails from applications being hosted on services such as Amazon EC2. SES also works seamlessly with Amazon Pinpoint, allowing for the creation of customer engagement communications that drive user activity and engagement.

How to build Generative AI into marketing communications

Dynamic Audience Targeting and Segmentation: Generative AI can help marketers to dynamically target and segment their audience. It can analyze customer data and behavior to identify patterns and trends, which can then be used to create more targeted marketing campaigns. Using Amazon Sagemaker or the soon-to-be-available Amazon Bedrock and Amazon Titan Models, Generative AI can suggest labels for customers based on unstructured data. According to McKinsey, generative AI can analyze data and identify consumer behavior patterns to help marketers create appealing content that resonates with their audience.

Personalized Marketing: Generative AI can be used to automate the creation of marketing content. This includes generating text for blogs, social media posts, and emails, as well as creating images and videos. This can save marketers a significant amount of time and effort, allowing them to focus on other aspects of their marketing strategy. Where it really shines is the ability to productionize marketing content creation, reducing the needs for marketers to create multiple copies for different customer segments. Previously, marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food). Generative AI can automate this process, providing the opportunities to dynamically create these contents programmatically and automatically send out to the most relevant segments via Amazon Pinpoint or Amazon SES.

Marketing Automation: Generative AI can automate various aspects of marketing, such as email marketing, social media marketing, and search engine marketing. This includes automating the creation and distribution of marketing content, as well as analyzing the performance of marketing campaigns. Amazon Pinpoint currently automates customer communications using journeys which is a customized, multi-step engagement experience. Generative AI could create a Pinpoint journey based on customer engagement data, engagement parameters and a prompt. This enables GenAI to not only personalize the content but create a personalized omnichannel experience that can extend throughout a period of time. It then becomes possible that journeys are created dynamically by generative AI and A/B tested on the fly to achieve an optimal pre-defined Key Performance Indicator (KPI).

A Sample Generative AI Use Case in Marketing Communications

AWS services are designed to work together, making it easy to implement generative AI in your marketing strategies. For instance, you can use Amazon SageMaker to build and train your generative AI models which assist with automating marketing content creation, and Amazon Pinpoint or Amazon SES to deliver the content to your customers.

Companies using AWS can theoretically supplement their existing workloads with generative AI capabilities without the needs for migration. The following reference architecture outlines a sample use case and showcases how Generative AI can be integrated into your customer journeys built on the AWS cloud. An e-commerce company can potentially receive many complaints emails a day. Companies spend a lot of money to acquire customers, it’s therefore important to think about how to turn that negative experience into a positive one.

GenAIMarketingSolutionArchitecture

When an email is received via Amazon SES (1), its content can be passed through to generative AI models using GANs to help with sentiment analysis (2). An article published by Amazon Science utilizes GANs for sentiment analysis for cases where a lack of data is a problem. Alternatively, one can also use Amazon Comprehend at this step and run A/B tests between the two models. The limitations with Amazon Comprehend would be the limited customizations you can perform to the model to fit your business needs.

Once the email’s sentiment is determined, the sentiment event is logged into Pinpoint (3), which then triggers an automatic winback journey (4).

Generative AI (e.g. HuggingFace’s Bloom Text Generation Models) can again be used here to dynamically create the content without needing to wait for the marketer’s input (5). Whereas marketers would need to generate many different copies for each granularity of customers (e.g. attriting customers who are between the age of 25-34 and loves food), generative AI provides the opportunities to dynamically create these contents on the fly given the above inputs.

Once the campaign content has been generated, the model pumps the template backs into Amazon Pinpoint (6), which then sends the personalized copy to the customer (7).

Result: Another customer is saved from attrition!

Conclusion

The landscape of generative AI is vast and ever-evolving, offering a plethora of opportunities for marketers to enhance their strategies and deliver more personalized, engaging content. AWS plays a pivotal role in this landscape, providing a comprehensive suite of services that facilitate the implementation of generative AI in marketing. From building and training AI models with Amazon SageMaker to delivering personalized messages with Amazon Pinpoint and Amazon SES, AWS provides the tools and infrastructure needed to harness the power of generative AI.

The potential of generative AI in relation to the marketer is immense. It offers the ability to automate content creation, personalize customer interactions, and derive valuable insights from data, among other benefits. However, it’s important to remember that while generative AI can automate certain aspects of marketing, it is not a replacement for human creativity and intuition. Instead, it should be viewed as a tool that can augment human capabilities and free up time for marketers to focus on strategy and creative direction.

Get started with Generative AI in marketing communications

As we conclude this exploration of generative AI and its applications in marketing, we encourage you to:

  • Brainstorm potential Generative AI use cases for your business. Consider how you can leverage generative AI to enhance your marketing strategies. This could involve automating content creation, personalizing customer interactions, or deriving insights from data.
  • Start leveraging generative AI in your marketing strategies with AWS today. AWS provides a comprehensive suite of services that make it easy to implement generative AI in your marketing strategies. By integrating these services into your workflows, you can enhance personalization, improve customer engagement, and drive better results from your campaigns.
  • Watch out for the next part in the series of integrating Generative AI into Amazon Pinpoint and SES. We will delve deeper into how you can leverage Amazon Pinpoint and SES together with generative AI to enhance your marketing campaigns. Stay tuned!

The journey into the world of generative AI is just beginning. As technology continues to evolve, so too will the opportunities for marketers to leverage AI to enhance their strategies and deliver more personalized, engaging content. We look forward to exploring this exciting frontier with you.

About the Author

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen

Tristan (Tri) Nguyen is an Amazon Pinpoint and Amazon Simple Email Service Specialist Solutions Architect at AWS. At work, he specializes in technical implementation of communications services in enterprise systems and architecture/solutions design. In his spare time, he enjoys chess, rock climbing, hiking and triathlon.

Alerting Rules!: InsightIDR Raises the Bar for Visibility and Coverage

Post Syndicated from Rapid7 original https://blog.rapid7.com/2023/07/06/alerting-rules-insightidr-raises-the-bar-for-visibility-and-coverage/

Alerting Rules!: InsightIDR Raises the Bar for Visibility and Coverage

By George Schneider, Information Security Manager at Listrak

I’ve worked in cybersecurity for over two decades, so I’ve seen plenty of platforms come and go—some even crash and burn. But Rapid7, specifically InsightIDR, has consistently performed above expectations. In fact, InsightIDR has become an essential resource for maintaining my company’s cybersecurity posture.

Alerting Rules!

Back in the early days, a SIEM didn’t come with a bunch of standardized alerting rules. We had to write all of our own rules to actually find what we were looking for. Today, instead of spending six hours a day hunting for threats, InsightIDR does a lot of the work for the practitioner. Now, we spend a maximum of one hour a day responding to alerts.

In addition to saving time, the out-of-the-box rules are very effective; they find things that our other security products can’t detect. This is a key reason I’ve been 100% happy with Rapid7. As a user, I just know it’s functional. It’s clear that InsightIDR is designed by and for users—there’s no fluff, and the kinks are already ironed out. Not only am I saving time and company resources, the solution is a joy to use.

Source Coverage

When scouting SIEM options, we wanted a platform that could ingest a lot of different log sources. Rapid7 covered all of the elements we use in the big platforms and various security appliances we have—and some in the cloud too. InsightIDR can ingest logs from all sources and correlate them (a key to any high-functioning SIEM) on day one.

Trust the Process

I can honestly say this is the first time I’ve ever used a product that adds new features and functionality every single quarter. It’s not just a new pretty interface either, Rapid7 consistently adds capabilities that move the product forward.

What’s also wonderful is that Rapid7 listens to customers, especially their feedback. Not to toot my own horn, but they’ve even released a handful of feature requests that I submitted over the years. So I can say with absolute sincerity that these improvements actually benefit SOC teams. They make us better at detecting the stuff that we’re most concerned about.

Visibility and Coverage, Thanks, Insight Agent!

If you’re not familiar with Insight Agent, it’s time to get acquainted. Insight Agent is critical for running forensics on a machine. If I have a machine that gets flagged for something through an automated alert, I can quickly jump in without delay because of the Insight Agent. I get lots of worthwhile information that helps me consistently finish investigations in a timely manner. I know in pretty short order whether an alert is nefarious or just a false positive.

And this is all built into the Rapid7 platform—it doesn’t require customization or installations to get up and running. You truly have a single pane of glass to do all of this, and it’s somehow super intuitive as well. Using the endpoint agent, I don’t have to switch over to something else to do additional work. It’s all right there.

“Customer support at Rapid7 is outstanding. It’s the gold standard that I now use to evaluate all other customer support.”

Thinking Outside the Pane

I also have to give a shout out to the Rapid7 community. The community at discuss.rapid7.com/ and the support I get from our Rapid7 account team cannot be overlooked. When I have a question about how to use something, my first step is to visit Discuss to see if somebody else has already posted some information about it—often saving me valuable time. If that doesn’t answer my question, the customer support at Rapid7 is outstanding. It’s the gold standard that I now use to evaluate all other customer support.

The Bottom Line

My bottom line? I love this product (and the people). To say it’s useful is an understatement. I would never recommend a product that I didn’t think was outstanding. I firmly believe in the Rapid7InsightIDR and experience how useful it is every day. So does my team.

To learn more about InsightIDR, our industry-leading cloud-native SIEM solution, watch this on-demand demo.

[$] BPF iterators for filesystems

Post Syndicated from original https://lwn.net/Articles/937326/

In the first of two combined BPF and filesystem sessions at the
2023 Linux Storage, Filesystem,
Memory-Management and BPF Summit, Hou Tao introduced his BPF iterators
for filesystem information. Iterators for
BPF are a relatively recent addition to the BPF landscape; they help
BPF programs step through kernel data structures in a loop-like manner, but
without running afoul of the BPF verifier, which is notoriously hard to
convince about loops.

Extract time series from satellite weather data with AWS Lambda

Post Syndicated from Lior Perez original https://aws.amazon.com/blogs/big-data/extract-time-series-from-satellite-weather-data-with-aws-lambda/

Extracting time series on given geographical coordinates from satellite or Numerical Weather Prediction data can be challenging because of the volume of data and of its multidimensional nature (time, latitude, longitude, height, multiple parameters). This type of processing can be found in weather and climate research, but also in applications like photovoltaic and wind power. For instance, time series describing the quantity of solar energy reaching specific geographical points can help in designing photovoltaic power plants, monitoring their operation, and detecting yield loss.

A generalization of the problem could be stated as follows: how can we extract data along a dimension that is not the partition key from a large volume of multidimensional data? For tabular data, this problem can be easily solved with AWS Glue, which you can use to create a job to filter and repartition the data, as shown at the end of this post. But what if the data is multidimensional and provided in a domain-specific format, like in the use case that we want to tackle?

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. With AWS Step Functions, you can launch parallel runs of Lambda functions. This post shows how you can use these services to run parallel tasks, with the example of time series extraction from a large volume of satellite weather data stored on Amazon Simple Storage Service (Amazon S3). You also use AWS Glue to consolidate the files produced by the parallel tasks.

Note that Lambda is a general purpose serverless engine. It has not been specifically designed for heavy data transformation tasks. We are using it here after having confirmed the following:

  • Task duration is predictable and is less than 15 minutes, which is the maximum timeout for Lambda functions
  • The use case is simple, with low compute requirements and no external dependencies that could slow down the process

We work on a dataset provided by EUMESAT: the MSG Total and Diffuse Downward Surface Shortwave Flux (MDSSFTD). This dataset contains satellite data at 15-minute intervals, in netcdf format, which represents approximately 100 GB for 1 year.

We process the year 2018 to extract time series on 100 geographical points.

Solution overview

To achieve our goal, we use parallel Lambda functions. Each Lambda function processes 1 day of data: 96 files representing a volume of approximately 240 MB. We then have 365 files containing the extracted data for each day, and we use AWS Glue to concatenate them for the full year and split them across the 100 geographical points. This workflow is shown in the following architecture diagram.

Deployment of this solution: In this post, we provide step-by-step instructions to deploy each part of the architecture manually. If you prefer an automatic deployment, we have prepared for you a Github repository containing the required infrastructure as code template.

The dataset is partitioned by day, with YYYY/MM/DD/ prefixes. Each partition contains 96 files that will be processed by one Lambda function.

We use Step Functions to launch the parallel processing of the 365 days of the year 2018. Step Functions helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines.

But before starting, we need to download the dataset and upload it to an S3 bucket.

Prerequisites

Create an S3 bucket to store the input dataset, the intermediate outputs, and the final outputs of the data extraction.

Download the dataset and upload it to Amazon S3

A free registration on the data provider website is required to download the dataset. To download the dataset, you can use the following command from a Linux terminal. Provide the credentials that you obtained at registration. Your Linux terminal could be on your local machine, but you can also use an AWS Cloud9 instance. Make sure that you have at least 100 GB of free storage to handle the entire dataset.

wget -c --no-check-certificate -r -np -nH --user=[YOUR_USERNAME] --password=[YOUR_PASSWORD] \
     -R "*.html, *.tmp" \
     https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/MDSSFTD/NETCDF/2018/

Because the dataset is quite large, this download could take a long time. In the meantime, you can prepare the next steps.

When the download is complete, you can upload the dataset to an S3 bucket with the following command:

aws s3 cp ./PRODUCTS/ s3://[YOUR_BUCKET_NAME]/ --recursive

If you use temporary credentials, they might expire before the copy is complete. In this case, you can resume by using the aws s3 sync command.

Now that the data is on Amazon S3, you can delete the directory that has been downloaded from your Linux machine.

Create the Lambda functions

For step-by-step instructions on how to create a Lambda function, refer to Getting started with Lambda.

The first Lambda function in the workflow generates the list of days that we want to process:

from datetime import datetime
from datetime import timedelta

def lambda_handler(event, context):
    '''
    Generate a list of dates (string format)
    '''
    
    begin_date_str = "20180101"
    end_date_str = "20181231"
    
    # carry out conversion between string 
    # to datetime object
    current_date = datetime.strptime(begin_date_str, "%Y%m%d")
    end_date = datetime.strptime(end_date_str, "%Y%m%d")

    result = []

    while current_date <= end_date:
        current_date_str = current_date.strftime("%Y%m%d")

        result.append(current_date_str)
            
        # adding 1 day
        current_date += timedelta(days=1)
      
    return result

We then use the Map state of Step Functions to process each day. The Map state will launch one Lambda function for each element returned by the previous function, and will pass this element as an input. These Lambda functions will be launched simultaneously for all the elements in the list. The processing time for the full year will therefore be identical to the time needed to process 1 single day, allowing scalability for long time series and large volumes of input data.

The following is an example of code for the Lambda function that processes each day:

import boto3
import netCDF4 as nc
import numpy as np
import pandas as pd
from datetime import datetime
import time
import os
import random

# Bucket containing input data
INPUT_BUCKET_NAME = "[INPUT_BUCKET_NAME]" # example: "my-bucket-name"
LOCATION = "[PREFIX_OF_INPUT_DATA_WITH_TRAILING_SLASH]" # example: "MSG/MDSSFTD/NETCDF/"

# Local output files
TMP_FILE_NAME = "/tmp/tmp.nc"
LOCAL_OUTPUT_FILE = "/tmp/dataframe.parquet"

# Bucket for output data
OUTPUT_BUCKET = "[OUTPUT_BUCKET_NAME]"
OUTPUT_PREFIX = "[PREFIX_OF_OUTPUT_DATA_WITH_TRAILING_SLASH]" # example: "output/intermediate/"

# Create 100 random coordinates
random.seed(10)
coords = [(random.randint(1000,2500), random.randint(1000,2500)) for _ in range(100)]

client = boto3.resource('s3')
bucket = client.Bucket(INPUT_BUCKET_NAME)

def date_to_partition_name(date):
    '''
    Transform a date like "20180302" to partition like "2018/03/02/"
    '''
    d = datetime.strptime(date, "%Y%m%d")
    return d.strftime("%Y/%m/%d/")

def lambda_handler(event, context):
    # Get date from input    
    date = str(event)
    print("Processing date: ", date)
    
    # Initialize output dataframe
    COLUMNS_NAME = ['time', 'point_id', 'DSSF_TOT', 'FRACTION_DIFFUSE']
    df = pd.DataFrame(columns = COLUMNS_NAME)
    
    prefix = LOCATION + date_to_partition_name(date)
    print("Loading files from prefix: ", prefix)
    
    # List input files (weather files)
    objects = bucket.objects.filter(Prefix=prefix)    
    keys = [obj.key for obj in objects]
           
    # For each file
    for key in keys:
        # Download input file from S3
        bucket.download_file(key, TMP_FILE_NAME)
        
        print("Processing: ", key)    
    
        try:
            # Load the dataset with netcdf library
            dataset = nc.Dataset(TMP_FILE_NAME)
            
            # Get values from the dataset for our list of geographical coordinates
            lats, lons = zip(*coords)
            data_1 = dataset['DSSF_TOT'][0][lats, lons]
            data_2 = dataset['FRACTION_DIFFUSE'][0][lats, lons]
    
            # Prepare data to add it into the output dataframe
            nb_points = len(lats)
            data_time = dataset.__dict__['time_coverage_start']
            time_list = [data_time for _ in range(nb_points)]
            point_id_list = [i for i in range(nb_points)]
            tuple_list = list(zip(time_list, point_id_list, data_1, data_2))
            
            # Add data to the output dataframe
            new_data = pd.DataFrame(tuple_list, columns = COLUMNS_NAME)
            df = pd.concat ([df, new_data])
        except OSError:
            print("Error processing file: ", key)
        
    # Replace masked by NaN (otherwise we cannot save to parquet)
    df = df.applymap(lambda x: np.NaN if type(x) == np.ma.core.MaskedConstant else x)    
        
    
    # Save to parquet
    print("Writing result to tmp parquet file: ", LOCAL_OUTPUT_FILE)
    df.to_parquet(LOCAL_OUTPUT_FILE)
    
    # Copy result to S3
    s3_output_name = OUTPUT_PREFIX + date + '.parquet'
    s3_client = boto3.client('s3')
    s3_client.upload_file(LOCAL_OUTPUT_FILE, OUTPUT_BUCKET, s3_output_name)

You need to associate a role to the Lambda function to authorize it to access the S3 buckets. Because the runtime is about a minute, you also have to configure the timeout of the Lambda function accordingly. Let’s set it to 5 minutes. We also increase the memory allocated to the Lambda function to 2048 MB, which is needed by the netcdf4 library for extracting several points at a time from satellite data.

This Lambda function depends on the pandas and netcdf4 libraries. They can be installed as Lambda layers. The pandas library is provided as an AWS managed layer. The netcdf4 library will have to be packaged in a custom layer.

Configure the Step Functions workflow

After you create the two Lambda functions, you can design the Step Functions workflow in the visual editor by using the Lambda Invoke and Map blocks, as shown in the following diagram.

In the Map state block, choose Distributed processing mode and increase concurrency limit to 365 in Runtime settings. This will enable parallel processing of all the days.

The number of Lambda functions that can run concurrently is limited for each account. Your account may have insufficient quota. You can request a quota increase.

Launch the state machine

You can now launch the state machine. On the Step Functions console, navigate to your state machine and choose Start execution to run your workflow.

This will trigger a popup in which you can enter optional input for your state machine. For this post, you can leave the defaults and choose Start execution.

The state machine should take 1–2 minutes to run, during which time you will be able to monitor the progress of your workflow. You can select one of the blocks in the diagram and inspect its input, output, and other information in real time, as shown in the following screenshot. This can be very useful for debugging purposes.

When all the blocks turn green, the state machine is complete. At this step, we have extracted the data for 100 geographical points for a whole year of satellite data.

In the S3 bucket configured as output for the processing Lambda function, we can check that we have one file per day, containing the data for all the 100 points.

Transform data per day to data per geographical point with AWS Glue

For now, we have one file per day. However, our goal is to get time series for every geographical point. This transformation involves changing the way the data is partitioned. From a day partition, we have to go to a geographical point partition.

Fortunately, this operation can be done very simply with AWS Glue.

  1. On the AWS Glue Studio console, create a new job and choose Visual with a blank canvas.

For this example, we create a simple job with a source and target block.

  1. Add a data source block.
  2. On the Data source properties tab, select S3 location for S3 source type.
  3. For S3 URL, enter the location where you created your files in the previous step.
  4. For Data format, keep the default as Parquet.
  5. Choose Infer schema and view the Output schema tab to confirm the schema has been correctly detected.

  1. Add a data target block.
  2. On the Data target properties tab, for Format, choose Parquet.
  3. For Compression type, choose Snappy.
  4. For S3 Target Location, enter the S3 target location for your output files.

We now have to configure the magic!

  1. Add a partition key, and choose point_id.

This tells AWS Glue how you want your output data to be partitioned. AWS Glue will automatically partition the output data according to the point_id column, and therefore we’ll get one folder for each geographical point, containing the whole time series for this point as requested.

To finish the configuration, we need to assign an AWS Identity and Access Management (IAM) role to the AWS Glue job.

  1. Choose Job details, and for IAM role¸ choose a role that has permissions to read from the input S3 bucket and to write to the output S3 bucket.

You may have to create the role on the IAM console if you don’t already have an appropriate one.

  1. Enter a name for our AWS Glue job, save it, and run it.

We can monitor the run by choosing Run details. It should take 1–2 minutes to complete.

Final results

After the AWS Glue job succeeds, we can check in the output S3 bucket that we have one folder for each geographical point, containing some Parquet files with the whole year of data, as expected.

To load the time series for a specific point into a pandas data frame, you can use the awswrangler library from your Python code:

import awswrangler as wr
import pandas as pd

# Retrieving the data directly from Amazon S3
df = wr.s3.read_parquet("s3://[BUCKET]/[PREFIX]/", dataset=True)

If you want to test this code now, you can create a notebook instance in Amazon SageMaker, and then open a Jupyter notebook. The following screenshot illustrates running the preceding code in a Jupyter notebook.

As we can see, we have successfully extracted the time series for specific geographical points!

Clean up

To avoid incurring future charges, delete the resources that you have created:

  • The S3 bucket
  • The AWS Glue job
  • The Step Functions state machine
  • The two Lambda functions
  • The SageMaker notebook instance

Conclusion

In this post, we showed how to use Lambda, Step Functions, and AWS Glue for serverless ETL (extract, transform, and load) on a large volume of weather data. The proposed architecture enables extraction and repartitioning of the data in just a few minutes. It’s scalable and cost-effective, and can be adapted to other ETL and data processing use cases.

Interested in learning more about the services presented in this post? You can find hands-on labs to improve your knowledge with AWS Workshops. Additionally, check out the official documentation of AWS Glue, Lambda, and Step Functions. You can also discover more architectural patterns and best practices at AWS Whitepapers & Guides.

About the Author

Lior Perez is a Principal Solutions Architect on the Enterprise team based in Toulouse, France. He enjoys supporting customers in their digital transformation journey, using big data and machine learning to help solve their business challenges. He is also personally passionate about robotics and IoT, and constantly looks for new ways to leverage technologies for innovation.

[$] Large folios for anonymous memory

Post Syndicated from original https://lwn.net/Articles/937239/

The transition to folios has transformed
the memory-management subsystem in a number of ways, but has also resulted
in a lot of code churn that has not been welcomed by all developers. As
this work proceeds, though, some of the benefits from it are beginning to
become clear. One example may well be in the handling of anonymous memory,
as can be seen in a pair of patch sets from Ryan Roberts.

Implementing AWS Lambda error handling patterns

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/implementing-aws-lambda-error-handling-patterns/

This post is written by Jeff Chen, Principal Cloud Application Architect, and Jeff Li, Senior Cloud Application Architect

Event-driven architectures are an architecture style that can help you boost agility and build reliable, scalable applications. Splitting an application into loosely coupled services can help each service scale independently. A distributed, loosely coupled application depends on events to communicate application change states. Each service consumes events from other services and emits events to notify other services of state changes.

Handling errors becomes even more important when designing distributed applications. A service may fail if it cannot handle an invalid payload, dependent resources may be unavailable, or the service may time out. There may be permission errors that can cause failures. AWS services provide many features to handle error conditions, which you can use to improve the resiliency of your applications.

This post explores three use-cases and design patterns for handling failures.

Overview

AWS Lambda, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and Amazon EventBridge are core building blocks for building serverless event-driven applications.

The post Understanding the Different Ways to Invoke Lambda Functions lists the three different ways of invoking a Lambda function: synchronous, asynchronous, and poll-based invocation. For a list of services and which invocation method they use, see the documentation.

Lambda’s integration with Amazon API Gateway is an example of a synchronous invocation. A client makes a request to API Gateway, which sends the request to Lambda. API Gateway waits for the function response and returns the response to the client. There are no built-in retries or error handling. If the request fails, the client attempts the request again.

Lambda’s integration with SNS and EventBridge are examples of asynchronous invocations. SNS, for example, sends an event to Lambda for processing. When Lambda receives the event, it places it on an internal event queue and returns an acknowledgment to SNS that it has received the message. Another Lambda process reads events from the internal queue and invokes your Lambda function. If SNS cannot deliver an event to your Lambda function, the service automatically retries the same operation based on a retry policy.

Lambda’s integration with SQS uses poll-based invocations. Lambda runs a fleet of pollers that poll your SQS queue for messages. The pollers read the messages in batches and invoke your Lambda function once per batch.

You can apply this pattern in many scenarios. For example, your operational application can add sales orders to an operational data store. You may then want to load the sales orders to your data warehouse periodically so that the information is available for forecasting and analysis. The operational application can batch completed sales as events and place them on an SQS queue. A Lambda function can then process the events and load the completed sale records into your data warehouse.

If your function processes the batch successfully, the pollers delete the messages from the SQS queue. If the batch is not successfully processed, the pollers do not delete the messages from the queue. Once the visibility timeout expires, the messages are available again to be reprocessed. If the message retention period expires, SQS deletes the message from the queue.

The following table shows the invocation types and retry behavior of the AWS services mentioned.

AWS service example Invocation type Retry behavior
Amazon API Gateway Synchronous No built-in retry, client attempts retries.

Amazon SNS

Amazon EventBridge

 Asynchronous Built-in retries with exponential backoff.
Amazon SQS Poll-based Retries after visibility timeout expires until message retention period expires.

There are a number of design patterns to use for poll-based and asynchronous invocation types to retain failed messages for additional processing. These patterns can help you recover from delivery or processing failures.

You can explore the patterns and test the scenarios by deploying the code from this repository which uses the AWS Cloud Development Kit (AWS CDK) using Python.

Lambda poll-based invocation pattern

When using Lambda with SQS, if Lambda isn’t able to process the message and the message retention period expires, SQS drops the message. Failure to process the message can be due to function processing failures, including time-outs or invalid payloads. Processing failures can also occur when the destination function does not exist, or has incorrect permissions.

You can configure a separate dead-letter queue (DLQ) on the source queue for SQS to retain the dropped message. A DLQ preserves the original message and is useful for analyzing root causes, handling error conditions properly, or sending notifications that require manual interventions. In the poll-based invocation scenario, the Lambda function itself does not maintain a DLQ. It relies on the external DLQ configured in SQS. For more information, see Using Lambda with Amazon SQS.

The following shows the design pattern when you configure Lambda to poll events from an SQS queue and invoke a Lambda function.

Lambda synchronously polling catches of messages from SQS

Lambda synchronously polling batches of messages from SQS

To explore this pattern, deploy the code in this repository. Once deployed, you can use this instruction to test the pattern with the happy and unhappy paths.

Lambda asynchronous invocation pattern

With asynchronous invokes, there are two failure aspects to consider when using Lambda. The event source cannot deliver the message to Lambda and the Lambda function errors when processing the event.

Event sources vary in how they handle failures delivering messages to Lambda. If SNS or EventBridge cannot send the event to Lambda after exhausting all their retry attempts, the service drops the event. You can configure a DLQ on an SNS topic or EventBridge event bus to hold the dropped event. This works in the same way as the poll-based invocation pattern with SQS.

Lambda functions may then error due to input payload syntax errors, duration time-outs, or the function throws an exception such as a data resource not available.

For asynchronous invokes, you can configure how long Lambda retains an event in its internal queue, up to 6 hours. You can also configure how many times Lambda retries when the function errors, between 0 and 2. Lambda discards the event when the maximum age passes or all retry attempts fail. To retain a copy of discarded events, you can configure either a DLQ or, preferably, a failed-event destination as part of your Lambda function configuration.

A Lambda destination enables you to specify what to do next if an asynchronous invocation succeeds or fails. You can configure a destination to send invocation records to SQS, SNS, EventBridge, or another Lambda function. Destinations are preferred for failure processing as they support additional targets and include additional information. A DLQ holds the original failed event. With a destination, Lambda also passes details of the function’s response in the invocation record. This includes stack traces, which can be useful for analyzing the root cause.

Using both a DLQ and Lambda destinations

You can apply this pattern in many scenarios. For example, many of your applications may contain customer records. To comply with the California Consumer Privacy Act (CCPA), different organizations may need to delete records for a particular customer. You can set up a consumer delete SNS topic. Each organization creates a Lambda function, which processes the events published by the SNS topic and deletes customer records in its managed applications.

The following shows the design pattern when you configure an SNS topic as the event source for a Lambda function, which uses destination queues for success and failure process.

SNS topic as event source for Lambda

SNS topic as event source for Lambda

You configure a DLQ on the SNS topic to capture messages that SNS cannot deliver to Lambda. When Lambda invokes the function, it sends details of the successfully processed messages to an on-success SQS destination. You can use this pattern to route an event to multiple services for simpler use cases. For orchestrating multiple services, AWS Step Functions is a better design choice.

Lambda can also send details of unsuccessfully processed messages to an on-failure SQS destination.

A variant of this pattern is to replace an SQS destination with an EventBridge destination so that multiple consumers can process an event based on the destination.

To explore how to use an SQS DLQ and Lambda destinations, deploy the code in this repository. Once deployed, you can use this instruction to test the pattern with the happy and unhappy paths.

Using a DLQ

Although destinations is the preferred method to handle function failures, you can explore using DLQs.

The following shows the design pattern when you configure an SNS topic as the event source for a Lambda function, which uses SQS queues for failure process.

Lambda invoked asynchonously

Lambda invoked asynchonously

You configure a DLQ on the SNS topic to capture the messages that SNS cannot deliver to the Lambda function. You also configure a separate DLQ for the Lambda function. Lambda saves an unsuccessful event to this DLQ after Lambda cannot process the event after maximum retry attempts.

To explore how to use a Lambda DLQ, deploy the code in this repository. Once deployed, you can use this instruction to test the pattern with happy and unhappy paths.

Conclusion

This post explains three patterns that you can use to design resilient event-driven serverless applications. Error handling during event processing is an important part of designing serverless cloud applications.

You can deploy the code from the repository to explore how to use poll-based and asynchronous invocations. See how poll-based invocations can send failed messages to a DLQ. See how to use DLQs and Lambda destinations to route and handle unsuccessful events.

Learn more about event-driven architecture on Serverless Land.

Belgian Tax Hack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/belgian-tax-hack.html

Here’s a fascinating tax hack from Belgium (listen to the details here, episode #484 of “No Such Thing as a Fish,” at 28:00).

Basically, it’s about a music festival on the border between Belgium and Holland. The stage was in Holland, but the crowd was in Belgium. When the copyright collector came around, they argued that they didn’t have to pay any tax because the audience was in a different country. Supposedly it worked.

Running a workshop with teachers to create culturally relevant Computing lessons

Post Syndicated from Katharine Childs original https://www.raspberrypi.org/blog/research-teacher-workshop-culturally-relevant-computing-lessons/

Who chooses to study Computing? In England, data from GCSE and A level Computer Science entries in 2019 shows that the answer is complex. Black Caribbean students were one of the most underrepresented groups in the subject, while pupils from other ethnic backgrounds, such as White British, Chinese, and Asian Indian, were well-represented. This picture is reflected in the STEM workforce in England, where Black people are also underrepresented.

Two young girls, one of them with a hijab, do a Scratch coding activity together at a desktop computer.

That’s why one of our areas of academic research aims to support Computing teachers to use culturally relevant pedagogy to design and deliver equitable learning experiences that enable all learners to enjoy and succeed in Computing and Computer Science at school. Our previous research projects within this area have involved developing guidelines for culturally relevant and responsive teaching, and exploring how a small group of primary and secondary Computing teachers used these guidelines in their teaching.

A tree symbolising culturally relevant pedagogy,with the roots labeled 'curriculum, the trunk labeled 'teaching approaches', and the crown labeled 'learning materials'.
Learning materials, teaching approaches, and the curriculum as a whole are three areas where culturally relevance is important.

In our latest research study, funded by Cognizant, we worked with 13 primary school teachers in England on adapting computing lessons to incorporate culturally relevant and responsive principles and practices. Here’s an insight into the workshop we ran with them, and what the teachers and we have taken away from it.

Adapting lesson materials based on culturally relevant pedagogy

In the group of 13 England-based primary school Computing teachers we worked with for this study:

  • One third were specialist primary Computing teachers, and the other two thirds were class teachers who taught a range of subjects
  • Some acted as Computing subject lead or coordinator at their school
  • Most had taught Computing for between three and five years 
  • The majority worked in urban areas of England, at schools with culturally diverse catchment areas 

In November 2022, we held a one-day workshop with the teachers to introduce culturally relevant pedagogy and explore how to adapt two six-week units of computing resources.

An example of a collaborative activity from a teacher-focused workshop around culturally relevant pedagogy.
An example of a collaborative activity from the workshop

The first part of the workshop was a collaborative, discussion-based professional development session exploring what culturally relevant pedagogy is. This type of pedagogy uses equitable teaching practices to:

  • Draw on the breadth of learners’ experiences and cultural knowledge
  • Facilitate projects that have personal meaning for learners
  • Develop learners’ critical consciousness

The rest of the workshop day was spent putting this learning into practice while planning how to adapt two units of computing lessons to make them culturally relevant for the teachers’ particular settings. We used a design-based approach for this part of the workshop, meaning researchers and teachers worked collaboratively as equal stakeholders to decide on plans for how to alter the units.

We worked in four groups, each with three or four teachers and one or two researchers, focusing on one of two units of work from The Computing Curriculum for teaching digital skills: a unit on photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).

Descriptions of a classroom unit of teaching materials about photo editing for Year 4 (ages 8–9), and a unit about vector graphics for Year 5 (ages 9–10).
We based the workshop around two Computing Curriculum units that cover digital literacy skills.

In order to plan how the resources in these units of work could be made culturally relevant for the participating teachers’ contexts, the groups used a checklist of ten areas of opportunity. This checklist is a result of one of our previous research projects on culturally relevant pedagogy. Each group used the list to identify a variety of ways in which the units’ learning objectives, activities, learning materials, and slides could be adapted. Teachers noted down their ideas and then discussed them with their group to jointly agree a plan for adapting the unit.

By the end of the day, the groups had designed four really creative plans for:

  • A Year 4 unit on photo editing that included creating an animal to represent cultural identity
  • A Year 4 unit on photo editing that included creating a collage all about yourself 
  • A Year 5 unit on vector graphics that guided learners to create their own metaverse and then add it to the class multiverse
  • A Year 5 unit on vector graphics that contextualised the digital skills by using them in online activities and in video games

Outcomes from the workshop

Before and after the workshop, we asked the teachers to fill in a survey about themselves, their experiences of creating computing resources, and their views about culturally relevant resources. We then compared the two sets of data to see whether anything had changed over the course of the workshop.

A teacher attending a training workshop laughs as she works through an activity.
The workshop was a positive experience for the teachers.

After teachers had attended the workshop, they reported a statistically significant increase in their confidence levels to adapt resources to be culturally relevant for both themselves and others. 

Teachers explained that the workshop had increased their understanding of culturally relevant pedagogy and of how it could impact on learners. For example, one teacher said:

“The workshop has developed my understanding of how culturally adapted resources can support pupil progress and engagement. It has also highlighted how contextual appropriateness of resources can help children to access resources.” – Participating teacher

Some teachers also highlighted how important it had been to talk to teachers from other schools during the workshop, and how they could put their new knowledge into practice in the classroom:

“The dedicated time and value added from peer discourse helped make this authentic and not just token activities to check a box.” – Participating teacher

“I can’t wait to take some of the work back and apply it to other areas and subjects I teach.” – Participating teacher

What you can expect to see next from this project

After our research team made the adaptations to the units set out in the four plans made during the workshop, the adapted units were delivered by the teachers to more than 500 Year 4 and 5 pupils. We visited some of the teachers’ schools to see the units being taught, and we have interviewed all the teachers about their experience of delivering the adapted materials. This observational and interview data, together with additional survey responses, will be analysed by us, and we’ll share the results over the coming months.

A computing classroom filled with learners
As part of the project, we observed teachers delivering the adapted units to their learners.

In our next blog post about this work, we will delve into the fascinating realm of parental attitudes to culturally relevant computing, and we’ll explore how embracing diversity in the digital landscape is shaping the future for both children and their families. 

We’ve also written about this professional development activity in more detail in a paper to be published at the UKICER conference in September, and we’ll share the paper once it’s available.

Finally, we are grateful to Cognizant for funding this academic research, and to our cohort of primary computing teachers for their enthusiasm, energy, and creativity, and their commitment to this project.

The post Running a workshop with teachers to create culturally relevant Computing lessons appeared first on Raspberry Pi Foundation.

Гешев вдига дървен меч

Post Syndicated from Емилия Милчева original https://www.toest.bg/geshev-vdiga-durven-mech/

Гешев вдига дървен меч

По-консервативни от консерваторите на Европейската народна партия, по-умерени от Джорджа Мелони и нейните „Италиански братя“, патриоти и родолюбци като ВМРО, без изявената путинофилия на „Възраждане“ и техния антиамериканизъм, но все пак с доза евроскептицизъм. Що е то?

Това е нещо като „Справедливост за България“ – обявеното от бившия главен прокурор Иван Гешев „гражданско, родолюбиво, консервативно движение, основано на националното достойнство, християнска етика и български традиционни ценности“. А, да, и малко социален консерватизъм (в стила на полската „Право и справедливост“ на Качински), защото без него няма как в държава, която е най-далеч от европейските средни доходи – истински десни в България няма. „Бог, Отечество, Семейство“ e мотото на формацията, обявено във Facebook от Гешев, който няма сключен граждански брак с жената, с която живее и от която има две деца.

Сред учредителите на сдружението с нестопанска цел са, освен Гешев, и издателят на сайта ПИК Недялко Недялков, Евгений Сачев – баща на депутатката от ГЕРБ Деница Сачева, председателката на разпуснатия вече граждански съвет към ВСС Мария Карагьозова, бившата депутатка от ГЕРБ Лилия Радева и Мая Живкова-Роджърс. На адреса на ПИК се намират и офисите на сдружението.

Ако се яви на избори, ще търси избиратели сред тези на ГЕРБ, но теоретично може да вземе и от други партии, които нямат твърд електорат, а симпатизантите им прескачат в последните години от ИТН към „Продължаваме промяната“, а после към „Възраждане“.

На този терен – на консерваторите-(антиглобалисти)-националисти, в последните години се появиха много политически сили: Цветан Цветанов и неговите „Републиканци за България“, „Възраждане“, КОД, „Има такъв народ“, новата партия „Eдинение“ на бившия зам.-министър на земеделието Иван Христанов, но имаше и НФСБ, и „Ред, законност, справедливост“. България следва европейската тенденция на изблик на консервативни антиглобалистки партии, противници на либерализма, някои от тях крайнодесни.

Формацията на Гешев също се заявява срещу неолибералите – и за независима съдебна власт, мажоритарни избори и традиционни ценности, тъй като „съзнателно искат да забравим корените си“ (неясно кои са искащите). Така, след като не успя да измете „политическия боклук“ – фраза, която стана формалната причина да бъде освободен от поста на главен прокурор, – Иван Гешев реши да влезе в политиката, за да мете там.  

Справедливост за кого?

„Не мир дойдох да донеса, а меч“, се казва в Евангелието на Матея (10:34). И Гешев така – само че мечът е дървен. Доскоро най-силният човек в държавата днес е шеф на политически стартъп с неясно и недотам светло бъдеще. Мечът в логото на сдружението напомня на меча в герба на ДС, който пък е правен по аналогия с този на КГБ, разкри във Facebook журналистът от „Сега“ Иво Балев. Други намериха творчески сходства с меча от фирмата на Слави Трифонов – ⅞.

Само пиар би предсказал успех на Гешевата формация и същественият аргумент е, че никой не свързва Гешев със справедливост. Той не успя да извоюва справедливост, когато беше най-овластеният и неконтролиран институционално човек в държавата, а сега, като политическа пешка, шансовете са минимални. В битката с олигархичния модел, овладял държавата, обществото не видя смелост, а умения да изпълнява поръчки. Той пък оневини бездействието си:

Като главен прокурор знам всичко за всеки – такава ми беше работата, но това не значи, че можеш да го докажеш. Никоя прокуратура не може да се пребори с кражбите и корупцията, далаверите, ако са превърнати в държавна политика.

Но с тези натрупани знания и без всякакви опити за противоречия с „държавната политика“ Иван Гешев изкара половината от 7-годишния си мандат. А днес цитира „Тютюн“ от Димитър Димов:

Над околийския началник стоеше областният, над областния – министърът, над министъра – правителството, а над правителството – мафията – невидима, всемогъща и безчовечна!

И ако обществото се е питало кой е стоял над главния прокурор – не е Господ, мафията е.

Имунитетът на Борисов

„Всички виждаме, че Народното събрание се крепи само защото го превърнаха в убежище за имунитети“, заяви Иван Гешев в публичното си изявление, прекъсвано на два пъти от спирания на тока. Призова парламента да публикува на сайта си внесените от него документи по „Барселонагейт“ – делото, по което искаше имунитета на Борисов, „тъй като не са секретни и всеки български гражданин да се запознае има или няма доказателства и дали биха издържали в съда“. Даде да се разбере, че свалянето му от поста било задвижено, когато изтекла информация за „Барселонагейт“.

В деня след изявлението на бившия главен прокурор Борисов най-напред обяви, че няма да даде имунитета си на съучениците на Гешев (прокурорът по делото е негов съученик, по признание на самия Гешев – б.а.), а броени часове след това каза точно обратното – че ще се откаже, за да не става „жертва на шантаж“. Така се слага край на нескончаемите усилия на парламентарната временна комисия за имунитетите да манкира, за да не стигне до обсъждането им, също и на обвиненията за „коалицията на имунитетите“.

Факт е обаче, че отказът на Борисов идва, когато изпълняващ функциите главен прокурор е Борислав Сарафов, заместник на Гешев и допреди два месеца близък негов съратник, а от два месеца – неприятел. Срещу Сарафов има подадени сигнали, след като влязоха в сила законовите промени, предвиждащи механизъм за разследване на главния прокурор и неговите заместници. Сигналите срещу Сарафов са свързани с имотни сделки „на близки и членове на семейството му“ (относно публикации за скъпи имотни покупки от страна на сина му – б.а.).

Дълговременният заместник

Изгледите Сарафов да остане дълго на поста зависят от това дали Висшият съдебен съвет, започнал процедура за избор на нов главен прокурор, ще я спре след проекта на декларация, внесена в парламента от ГЕРБ–СДС, ПП–ДБ и ДПС, в която трите формации поискаха спиране на процедурата.

Мотивите им са, че изборът трябва да се проведе, след като Конституционният съд се произнесе по жалбата срещу новия начин за избиране, освобождаване и разследване на главният прокурор и след като бъдат приети евентуални промени в Конституцията относно правомощията му. Според публикувания от ВСС график изслушването и изборът на нов главен прокурор трябва да станат на 26 октомври. Декларацията, дори и да се гласува от парламента, не е задължаваща за независимата по закон съдебна власт.

„Не е логично Съвет с изтекъл мандат, независимо че е легитимен към момента, да избира прокурор за 7 години. Първо да се приемат промените в Конституцията, след това нов ВСС и след това нов главен прокурор“, заяви и депутатът от ДПС Делян Пеевски, известен като един от „кръстниците“ на съдебната система, санкциониран по антикорупционния американски закон „Магнитски“, а в новото си качество – конституционен реформатор. Същият Пеевски, чието име не беше прочетено от Гешев зад инициалите Д.П. в документите за грабежа „КТБ“.

Но ще спази ли изобщо парламентът срока от 3 месеца за избор на нов ВСС, заложен в приетите в края на май промени в Haĸaзaтeлнo-пpoцecyaлния ĸoдeĸc и Зaĸoнa зa cъдeбнaтa влacт, с които беше въведен и механизмът зa ĸoнтpoл нa глaвния пpoĸypop? Останаха два месеца, единият от които е парламентарна ваканция, а изборът на членове на регулатори и институции се оказва най-спорната част в не-коалиционните отношения между ГЕРБ–СДС и ПП–ДБ. Пред БНР адв. Валя Гигова заяви:

Главният прокурор не е случаен административен ръководител и не може да бъде избран от ВСС, който е компрометиран като авторитет.

Предстоят трудни седмици за управляващите, които се утвърждават и чрез заявената от тях съдебна и конституционна реформа. Сарафов е второ издание на Гешев. Разликата е, че още не му е свален намордникът. Какви качества трябва да притежава бъдещият главен прокурор, за да няма повече реплики като „Ти си го избра“? Със сигурност нито едно от притежаваните от Гешевсарафов.

Преводът не е веднъж завинаги

Post Syndicated from Габриела Манова original https://www.toest.bg/prevodut-ne-e-vednuzh-zavinagi/

Преводът не е веднъж завинаги

Анджела Родел е родена в Уисконсин, САЩ, през 1974 г. Живее и работи в България от близо 20 години. Превеждала е знакови имена в българската литература – като Георги Господинов, Ивайло Петров, Георги Марков, Захари Карабашлиев, Ангел Игов, Милен Русков, Вера Мутафчиева и др. През май 2023 г. заедно с Георги Господинов печели Международния Букър – най-престижната награда за художествена литература в превод на английски език.

С Анджела Родел разговаря Габриела Манова

Срещам се с Анджела Родел в офиса ѝ в топъл юнски ден. Питам я харесва ли ѝ горещината, защото знам, че е родом от доста по-студено място. Засмива се, казва, че дори тя се предава и пуска климатик – все пак идва от суперклиматизираната Америка. Америка. За много българи американската мечта е още жива и мнозина са заминали да търсят щастието си отвъд океана.

Но Анджела е избрала да дойде тук. И да остане.

Едно от първите ѝ посещения в България е по време на Виденовата зима. Чудя се как не се е отказала. Разказва ми, че преди това отишла в Копривщица през 95-та, а там „супер, някакви планини, някакви баби, слънце, беше много така, романтично и идилично, и вече като кандидатствах за „Фулбрайт“¹ през 96–97-ма, точно за Виденовата зима бях тук“.

Преживяването я заземява: „За мен беше страхотен урок, бях на 23 и разбрах каква щастливка съм, че това, което приемаме за даденост в Америка, хич не е даденост. Тогава имах много нереална, романтична представа. А за мен беше просто по-дълбоко запознаване с България.“ Сравнява го с обичта към човек – в един момент виждаш всичките му страни – и добрите, и лошите, но това не те отблъсква, просто го приемаш такъв, какъвто е. „Благодарна съм, че бях тук през този период. Мисля, че получих някакво прозрение и за себе си, и за България.“

Питам я дали вярва, че човек има определен път, по който върви, и каквото и да прави, се озовава на него, или всичко е плод на случайност. „Бях в Америка и правех докторантура, но през цялото време чувствах, че нещо беше недовършено. Вече като дойдох през 2004–2005 г., пак се оказах на този кръстопът – дали да се връщам в Щатите и да защитавам дисертация, или да оставам – и просто реших да остана. Не беше много рационално решение, имаше някакво силно вътрешно чувство, че моята работа тук не е свършена, че има какво да уча от това място.“

На 23 май, в навечерието на най-хубавия български празник, Георги Господинов и Анджела Родел спечелиха Международния Букър за романа „Времеубежище“ и превода му на английски език. Поздравявам я за изключителното признание и споделям, че още в момента, в който Лейла Слимани обяви, че романът, избран от журито, е отличен и заради силно поетичния си език, си казах: няма как да не е „Времеубежище“. Наистина ли, отвръща Анджела, и казва, че с Георги са били изумени, че изобщо са стигнали до дългия списък. „Още ми се вижда като сън, не мога да повярвам.“ Питам я дали за нея е било мечта да спечели тази награда. „Дори не смеехме да си мечтаем. Когато станахме част от дългия списък, си казахме, че е голяма чест, и нали, дотук бяхме! Като влязохме и в краткия списък – пак: дотук бяхме! Аз лично не посмях да си мечтая такова нещо, но…“

Споделям с Анджела надеждите си високото признание да окаже влияние върху самочувствието на българите по отношение на българската литература. „Както каза Георги в много интервюта,

това би трябвало да отвори вратата и за млади писатели, и за други български писатели; вече ще бъдем на картата…“

Интересно ми е какви са според нея впечатленията на американците от България и българската литература. „Средният американец не е чувал за България, не знае къде е. Може би сега, след като вече има толкова българи в Америка, си казват: А, да, имам един приятел, той е българин, тук, на работа, но като цяло никакви асоциации нямат.“ Надява се Международният Букър да е „вратичка към източноевропейската литература“.

„В САЩ реалността е такава, че процентът преводна литература, която се издава, е много малък. И той включва цялата нехудожествена литература, преводни учебници, научни трудове, справочници и т.н. Докато в България дори през социализма винаги е имало много преводна литература. И като гледам пазара, много се следи. Все пак мисля, че европейците като цяло, включително България, са по-отворени и на преводната литература не се гледа като на втора ръка, докато в САЩ, поне допреди двайсетина години, да гледаш филм със субтитри беше някак под нивото им. Но новото поколение вече не мисли така.“

Разпитвам Анджела за първите ѝ стъпки в превода на български. Започнала е с поезия: „Това с превода на поезия стана случайно, но мисля, че има нещо вярно, че най-добрият преводач на поезия е поет, затова обичам да работя с български или американски поети като редактор. Чета поезия, но чувствам, че не е моето. Превела съм доста поезия, но винаги с чувство на притеснение…“ Анджела преподава в магистърската програма „Преводач-редактор“, където също превеждат поезия: „Всички винаги се притесняват, страхуват се, питат се как ще стане, но все се оказва, че има един-двама със страхотна дарба… Те сякаш са поети, още неоткрити, и преводачи, на които това е силната им страна.“

Във връзка с преподавателската работа я питам смята ли, че преводът може да бъде преподаван, и как протичат нейните лекции. Впечатлението ѝ е, че много студенти бързат към вълнуващи и интересни теми, например диалекти, жаргон, но според нея структурата на езика, граматиката, са основополагащи и често се оказват препъникамъни за младите преводачи. Признава, че дори тя, макар и опитна, винаги има едно наум за глаголните времена, когато превежда.

„Има стратегии, няма невъзможни неща за превод… почти,

но да, имам едно наум, че всеки текст съдържа капани.“

А кои са нейните учители в превода? Имало ли е въобще някой, който да се занимава с превод от български на английски във времето, когато е дошла в България? „Малко съжалявам, че не съм имала учител, може би по-бързо щях да стигна до някакви важни изводи, но като започнах да превеждам – през 2004–2005 г. работих в списание Vagabond – и там Антони Георгиев, който е много опитен, страшно ми помогна, даде ми кураж. Тогава, като новак, се придържах много буквално към текста, коя съм аз, че да… Нямах самочувствие да е по-леко, свободно, но Антони много ме окуражаваше. Ти знаеш този език, той е и твой, може да даваш нещо от себе си, и това беше много важно… че той като редактор минаваше през всички текстове и държеше преводът да е верен на оригиналния текст, но и да не е скован, дърварски – беше много полезен учител в това отношение. Самите автори също много ми помогнаха, даваха обратна връзка. Имах късмет да работя с автори, които от своя страна имаха търпение да работят с мен.“

С Георги Господинов например, изглежда, че имат невероятен синхрон, сякаш работата им върви много плавно. „Да, той е страхотен, може би от малкото български писатели, които наистина гледат на писането като на занаят. При него всичко е суперизящно, много рядко може да намериш нещо, което да не е както трябва. Активен е, участва, без да се обижда, без да се държи, сякаш бъркам в някаква рана, което е доста деликатен момент. Разбирам, че преводачът понякога е първият човек, който влиза в текста, в който пък понякога се случва да има несъответствия, проблеми, клишета. Винаги усещам дали авторът е отворен към такъв тип коментар. Има писатели, които са благодарни, казват: Търсил съм такъв редактор и не съм намерил…

Да си преводач от български на английски включва и доста посланическа работа. „Много е трудно и това е неплатен труд, нека бъдем откровени. Писах един текст за сборника на Димитър Камбуров и Михаела Харпър² и там точно за това говорех, че ти не си само преводач, никой не идва при теб да ти каже: Дай да преведем един български писател. Самият ти трябва да си скаут, да откриеш интересен автор, да инвестираш в превод, после ставаш агент, търсиш списания, издатели, евентуално намираш възможност за публикуване. И след това пак ти си този, който кандидатства за грантове, финансиране, ти си връзката с издателите… А когато, живот и здраве, книгата излезе, ако авторът не е много сигурен в английския си, трябва да ходиш по разни фестивали – което е супер! Примерно, с Вера Мутафчиева, която вече не е между живите, аз ще правя книжно турне, защото съм и рекламен агент, и пиар… Цялата работа включва много повече от чистата работа с текста.“

И като споменаваме Вера Мутафчиева, Анджела вече е на финалната права в редактирането на превода на „Случаят Джем“. Признава, че е било предизвикателство. „Имаше много турски вътре – нали Вера Мутафчиева е била османистка, учен, историк, и не дава нито бележки под линия, нито нищо, просто ти си в турския текст. Открих един турчин, с когото да се консултирам. Иначе езикът ѝ е страхотен, авторката е с много готино чувство за хумор. За съжаление, не съм я познавала лично, но явно е била добра психоложка, уловила е какво е характерното в човека, в нейните персонажи. Затова въпреки че беше трудно, беше и адски приятно за превод. А и текстът е много авангарден за времето си! През 60-те години такава структура! Как изобщо е излязло подобно нещо в социалистическа България…“ Може да прочетете и прекрасния текст, в който самата Анджела разказва за преживяването си с книгата.

Питам я как се е справила с изискването на американските издателства нищо да не се извежда под линия. „По принцип модата е такава, че не бива да има. Смята се, че това изважда читателя от текста, и преди, когато работехме с издателство Open Letters по „Физика на тъгата“, нямахме право една бележка под линия да сложим, което е много трудно – цялата книга е пълна със соцреалии, просто се видяхме в чудо. С новия издател – Norton, просто без да питаме, решихме, че не може. Но оттам ми пратиха някакви други свои книги, видях, че има бележки, и ги попитах: Ама може ли бележки под линия, а те: Да, естествено, може да използвате. С Георги вече бяхме толкова опарени от предишния си опит, че подходихме много пестеливо. Може би няма и десет бележки под линия в целия роман (Time Shelter).“

И все пак как се предава на американския читател целият този социализъм и въобще културата на Източния блок и мисленето на хората по онова време?

„Едно от спасенията за мен като преводач, е, че хората, които четат преводна литература, са доста подбрана публика;

те вече имат интерес, особено към източноевропейски автори. И понеже има толкова руски преводи от този период, надявам се, че тази атмосфера може да е позната и от други произведения. В България социализмът е различен в сравнение с останалите соцдържави, но идеите като цяло все пак имат някаква почва в американското въображение.“

Припомням ѝ за ателието по превод на Фондация „Елизабет Костова“ от 2020 г., в което лектори бяха самата Родел, Изидора Анжел, Екатерина Петрова и Велина Минкова. Тогава за пръв път осъзнах колко е трудно да бъдеш преводач от български на английски, колко усилия изисква това. Анджела признава, че Елизабет Костова и фондацията са я въвели в тази сфера и среда, благодарна е, че са ѝ помогнали да създаде контакти с издатели и редактори.

Споделям, че точно по време на въпросното ателие съм придобила малко увереност, че човек може да превежда и на език, който не му е майчин. „Мисля, че това е някакъв предразсъдък. Да, преди съществуваше нагласата, че трябва да превеждаш само на майчиния си език, но

хегемонията на native speakers³ в превода полека си отива.

Например тази година и аз, и Изидора [Анжел] бяхме избрани за стипендиантки на National Endowment for the Arts⁴, и от двайсетина проекта имаше около 6–7 души, които не бяха native speakers, но въпреки това спечелиха. Вече разбираме, че няма един английски, и това, че езикът не ти е роден, не означава, че не можеш да бъдеш добър преводач или че няма какво да дадеш на този текст. Нещата се променят за добро.“

Имало ли е текст или части от текст, за които си е казвала: Не, това не може да се преведе? „Да, по-скоро отделни моменти, и все се сещам за Иглика Василева, която, като превеждала „Одисей“, в един момент толкова се фрустрирала, че взела един нож и пробола книгата. И аз напълно я разбирам!

Сега например с „Хайка за вълци“ – толкова обичам този роман, просто брилянтен език, с чувство за хумор… Може би 7–8 години търсих издател, най-накрая намерих, и си викам: Ама не мога да я преведа тая книга, няма да стане на английски, какво правя аз тука… Не мога! И в един момент, като отшумя това отчаяние, си казах: Да, значи, няма да стане толкова готино, както е на български, с всички там диалекти, идиолекти, но все пак има толкова много интересни неща, чисто исторически, лични, социални, политически, че има стойност, дори всички тънки езикови работи да няма как да ги спася… Поне аз така се утеших.

Може би никой превод не е финален, може би след двайсет години ще има различни разбирания и нови преводи. Това е нормално, преводът не е веднъж завинаги.“

Като един истински ренесансов човек Анджела не се ограничава само с една форма на изкуство. Всъщност в България я довежда и страстта ѝ към музиката и пеенето. Питам я дали това ѝ помага да предаде ритъма на текста. „Абсолютно, аз съм напълно убедена, че има връзка. Бях в Лондон преди може би десет години за един преводачески семинар, след семинара пием бира и се оказва, че всеки от нас е и музикант, просто всеки. Това не е случайно, според мен ти помага страшно много да усетиш мелодията, звучността на езика, стига да имаш ухо за тия неща.“

Излиза, че различни пътища водят Анджела към преводаческата дейност. Може би просто когато нещо е истински важно за нас, намираме пътя към него през всичко, което правим.

1 Анджела Родел идва в България да учи български със стипендия „Фулбрайт“, а днес е изпълнителен директор на Българо-американската комисия за образователен обмен „Фулбрайт“.

2 Става дума за сборника Bulgarian Literature as World Literature.

3 Ползва се за хора, на които даден език им е майчин.

4 Служба на федералното правителство в САЩ и най-големият спонсор на изкуствата и обучението по изкуства.

Кюрдският въпрос и „бездомността“ на 30 милиона души

Post Syndicated from Александър Нуцов original https://www.toest.bg/kyurdskiyat-vupros-i-bezdomnostta-na-30-miliona-dushi/

Кюрдският въпрос и „бездомността“ на 30 милиона души

Кюрдите – най-многобройният народ в света без собствена държава, имат почти митичен ореол заради суровата си съдба, белязана от изтощителни борби за повече независимост и международно признание. Наброявайки над 30 млн. души, повечето кюрди днес обитават териториите на четири от водещите държави в региона на Близкия изток – Турция, Иран, Ирак и Сирия.

Сам по себе си този факт показва, че кюрдската общност не е хомогенна, а се състои от множество по-малки субекти на различна територия, които често имат разнородни убеждения, цели и дневен ред. Това възпрепятства опитите за всеобщо обединение около една-единствена и ясно формулирана национална кауза. За да обясним защо кюрдите остават силно маргинализирани в международните отношения и често нежелани в страните, които населяват, първо трябва да обърнем поглед към миналото, обуславящо днешната действителност.

Структурните причини за „бездомността“ на кюрдския народ ще открием в разпада на Османската империя и създаването на турската национална държава под ръководството на Мустафа Кемал Ататюрк. След края на Първата световна война и прекрояването на границите на бившата Османска империя кюрдите не създават собствена държава по силата на сключените мирни споразумения, а повечето от тях остават в пределите на новоустановената Турска република.

Силно милитаризираният, бюрократичен държавен апарат на младата турска държава по западен маниер налага принципа за равенство пред закона на всички нейни граждани. Зад това обаче прозира и идеята за приобщаване на разнородното население в Турция под шапката на обща национална (турска) идентичност, основаваща се на равни права пред Конституцията – за сметка на етническата идентичност на отделни групи от обществото (особено тези, които могат да нарушат териториалната цялост и суверенитета на страната). Това създава необходимите предпоставки за асимилация на малцинствата в духа на единството на турската нация и става изходната точка, от която се определят последвалите събития и противоборството между турската държава и кюрдските съпротивителни движения.

Кратка хронология

Локални кюрдски въстания има още преди началото на Първата световна война (1907 г. и 1914 г.). По време на войната обаче започва да се оформя идеята за установяване на независима кюрдска държава. Първоначално тя е подкрепена от съюзниците в Антантата – Великобритания и Русия, с цел да провокират вътрешна съпротива срещу техния военен враг – Османската империя. Избухналите въстанията по време на войната биват потушени, но са важни за покълването на кюрдския национализъм и за бъдещите съпротивителни движения.

След създаването на Турската република избухват нови неуспешни въстания през 1925 г. и в периода 1926–1930 г. Те дават повод на турската държава да оправдае извънредни ответни мерки, насочени срещу кюрдската общност и етническата ѝ същност, включително репресии, насилствена промяна на имената, изселване на кюрди от югоизточните турски територии, погазване на езикови и други малцинствени права, уредени в Лозанския договор от 1923 г. В отговор на това кюрдите започват да сформират редица съпротивителни движения, най-радикалното от които е добре познатата днес Кюрдска работническа партия (Partiya Karkeran Kurdistan, PKK), формално основана през 1974 г. от група студенти от кюрдски произход и обявена за терористична организация от различни държави и институции, сред които Турция, САЩ и Европейския съюз.

С началото на въоръжените действия на PKK през 1984 г. антагонизмът между Турция и кюрдската общност придобива крайни риторически и физически измерения. Влиза се в спирала от насилие, която унищожава пространствата за диалог и разрешаване на конфликта по мирен път – отвъд логиката на сигурността, насилието и войната. Неслучайно крехките моменти на примирие (например през периода 2012–2015 г.) лесно рухват под натиска на вътрешнополитическите и международните обстоятелства, въпреки че PKK променя изначалната си концепция и се отказва от идеята за независима държава за сметка на автономия и повече права.

Кюрдите днес

Кюрдите представляват хетерогенна общност. Тя се състои от граждани и граждански организации, партии, паравоенни формирования, племенни групи и др., различни по своя характер, цели и способи на действие. Това е и една от причините за изключително заплетеното положение на кюрдите във вътрешнополитически и международен план.

Една част от кюрдското население в Турция (общо около 15 млн.) е добре интегрирана в обществото и участва активно в него. По-гладко, разбира се, се приобщават младите хора в по-големите населени места, където връзката с етническите им корени и традиции напълно липсва или не е толкова устойчива. Въпреки това кюрдската общност остава силно маргинализирана най-вече по-отношение на своите езикови права. Кюрдският език и до днес не е признат за официален, въпреки че около 20% от турското население има кюрдски корени. От това произлиза и забраната за използването му за целите на официалното образование, което на практика ограничава конституционното право на образование на кюрдите, владеещи само майчиния си език.

На този фон интересите на кюрдската общност се защитават от легални политически формации със значително влияние в обществения живот. Най-силна сред тях е прокюрдската Демократична партия на народите, която през 2021 г. (заедно с други партии, граждански организации и активисти) поде нова кампания с искане за признаване на кюрдския език за официален заедно с правото да бъде използван в образователната система.

В анализа на Al-Monitor оттогава се посочва, че отношението на турските власти към кюрдското малцинство е променливо. В периода, в който Турция води активни преговори за присъединяване към Европейския съюз, рестриктивните мерки спрямо кюрдския език са смекчени, а в училищата дори е въведен избираем предмет по кюрдски език. През 2015 г. обаче, когато мирните преговори между Турция и PKK се провалят, а междувременно преговорите с ЕС биват замразени, държавната политиката спрямо кюрдите рязко се променя. Сред посочените примери в материалa са натискът върху родители да не записват децата си в избираемите курсове по кюрдски и чистката на ръководители с кюрдски корени в местните администрации (заради предполагаеми връзки с PKK) и замяната им с верни на Ердоган лица.

В този ред на мисли една от ключовите причини за маргинализацията на кюрдската общност е вътрешното идеологическо и риторическо противоборство между PKK и тази част от общността (вкл. партии, граждани и организации), които не одобряват насилието и предпочитат мирния подход при отстояване на интересите си.

Както вече отбелязахме, турската власт и PKK управляват конфликта през призмата на сигурността. Докато Ердоган разглежда кюрдите (и в частност PKK) като екзистенциална заплаха за турската териториална цялост и суверенитет, а PKK възприема властта като заплаха за съществуването на кюрдската идентичност, двете страни ще легитимират прилагането на извънредни мерки – насилие, от една страна, и репресии, от друга. Омагьосаният кръг драстично стеснява терена за действие на тази част от кюрдската общност, която се опитва да пречупи сегашната логика и да прехвърли спора в полето на разговора и мирния напредък в отношенията.

Между чука и наковалнята

Вътрешната хетерогенност е съпътствана от противоречия на териториален принцип. Ситуацията с кюрдите в Сирия и Ирак е коренно различна. Сирийските кюрди (около 10% от населението) активно участват във военните действия в страната, борейки се за автономия в рамките на Сирия, без да декларират намерение за създаване на независима кюрдска държава по границите между Турция, Сирия и Ирак. Кюрдите на практика контролират североизточните части на Сирия (историческия регион Рожава), където установяват нестабилна автономия.

Сирийските кюрди са между чука и наковалнята – между Турция, която им нанася военни удари и подпомага финансово и военно опозиционни групировки, борещи се за територия едновременно срещу Башар Асад и срещу кюрдите, и сирийския режим, с който поддържат крехък баланс в отношенията на основата на общия враг в лицето на подкрепяните от Турция бунтовнически сили. Кюрдите в Ирак (втори по численост етнос) също са си извоювали частична автономия в северните части на държавата. Те също се борят за по-силна автономия в контролираните от тях територии. Кюрдите в Иран са едно от най-големите малцинства на територията на страната. Водени от представляващите ги политически движения, те се превърнаха в основен двигател на протестните движения, обхванали цялата страна.

Поради невъзможността в един материал да разгледаме подробно характера и отношенията на многобройните играчи сред кюрдското население, ще направим следното обобщение: кюрдската общност е съставена от образувания с различна структура, роля и позиции на фона на особеностите в страните, които населяват. Във всяка от държавите съществуват кюрдски политически формации, повечето от които разполагат със собствени военни подразделения (например PYD и YPG в Сирия). Различните партийни, военни или граждански организации често имат лични, племенни, идеологически или управленски различия както вътрешно, така и в отношенията помежду си. И дори когато интересите им се преплитат, трудно установяват единно ръководство в преследване на обща цел.

Какво да следим?

Кюрдският въпрос се задълбочи с избухването на революцията в Сирия, когато кюрдите придобиха огромно международно значение като основна сила в борбата срещу тероризма в Близкия изток. Борбата им за автономия и повече права обаче е изправена пред огромни предизвикателства както по отношение на необходимостта от смекчаване на различията между отделните кюрдски общности, така и по отношение на преплитащите се, често променливи интереси на регионалните играчи (най-вече Турция, Сирия и Ирак) и цялата международната общност (в частност САЩ, ЕС, Русия и Китай).

При сегашните обстоятелства страховете на Турция от създаване на независима кюрдска държава изглеждат твърде преувеличени най-вече поради липсата на еднородна кюрдска политика. По-правдоподобният сценарий в средносрочен план е кюрдите да продължат борбите си по места. В този смисъл интересни за проследяване ще са темите за правата на кюрдите в Турция и ролята им в предстоящите местни избори през 2024 г., за ролята на сирийските кюрди в контекста на геополитическия сблъсък в страната, за борбата на иракските кюрди за запазване и разширяване на автономията, както и за ролята на кюрдите в Иран за изхода от конфликта между гражданите и властта.

Introducing the latest Machine Learning Lens for the AWS Well-Architected Framework

Post Syndicated from Raju Patil original https://aws.amazon.com/blogs/architecture/introducing-the-latest-machine-learning-lens-for-the-aws-well-architected-framework/

Today, we are delighted to introduce the latest version of the AWS Well-Architected Machine Learning (ML) Lens whitepaper. The AWS Well-Architected Framework provides architectural best practices for designing and operating ML workloads on AWS. It is based on six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and—a new addition to this revision—Sustainability. The ML Lens uses the Well-Architected Framework to outline the steps for performing an AWS Well-Architected review for your ML implementations.

The ML Lens provides a consistent approach for customers to evaluate ML architectures, implement scalable designs, and identify and mitigate technical risks. It covers common ML implementation scenarios and identifies key workload elements to allow you to architect your cloud-based applications and workloads according to the AWS best practices that we have gathered from supporting thousands of customer implementations.

The new ML Lens joins a collection of Well-Architected lenses that focus on specialized workloads such as the Internet of Things (IoT), games, SAP, financial services, and SaaS technologies. You can find more information in AWS Well-Architected Lenses.

What is the Machine Learning Lens?

Let’s explore the ML Lens across ML lifecycle phases, as the following figure depicts.

Machine Learning Lens

Figure 1. Machine Learning Lens

The Well-Architected ML Lens whitepaper focuses on the six pillars of the Well-Architected Framework across six phases of the ML lifecycle. The six phases are:

  1. Defining your business goal
  2. Framing your ML problem
  3. Preparing your data sources
  4. Building your ML model
  5. Entering your deployment phase
  6. Establishing the monitoring of your ML workload

Unlike the traditional waterfall approach, an iterative approach is required to achieve a working prototype based on the six phases of the ML lifecycle. The whitepaper provides you with a set of established cloud-agnostic best practices in the form of Well-Architected Pillars for each ML lifecycle phase. You can also use the Well-Architected ML Lens wherever you are on your cloud journey. You can choose either to apply this guidance during the design of your ML workloads, or after your workloads have entered production as a part of the continuous improvement process.

What’s new in the Machine Learning Lens?

  1. Sustainability Pillar: As building and running ML workloads becomes more complex and consumes more compute power, refining compute utilities and assessing your carbon footprint from these workloads grows to critical importance. The new pillar focuses on long-term environmental sustainability and presents design principles that can help you build ML architectures that maximize efficiency and reduce waste.
  2. Improved best practices and implementation guidance: Notably, enhanced guidance to identify and measure how ML will bring business value against ML operational cost to determine the return on investment (ROI).
  3. Updated guidance on new features and services: A set of updated ML features and services announced to-date have been incorporated into the ML Lens whitepaper. New additions include, but are not limited to, the ML governance features, the model hosting features, and the data preparation features. These and other improvements will make it easier for your development team to create a well-architected ML workloads in your enterprise.
  4. Updated links: Many documents, blogs, instructional and video links have been updated to reflect a host of new products, features, and current industry best practices to assist your ML development.

Who should use the Machine Learning Lens?

The Machine Learning Lens is of use to many roles, including:

  • Business leaders for a broader appreciation of the end-to-end implementation and benefits of ML
  • Data scientists to understand how the critical modeling aspects of ML fit in a wider context
  • Data engineers to help you use your enterprise’s data assets to their greatest potential through ML
  • ML engineers to implement ML prototypes into production workloads reliably, securely, and at scale
  • MLOps engineers to build and manage ML operation pipelines for faster time to market
  • Risk and compliance leaders to understand how the ML can be implemented responsibly by providing compliance with regulatory and governance requirements

Machine Learning Lens components

The Lens includes four focus areas:

1. The Well-Architected Machine Learning Design Principles

A set of best practices that are used as the basis for developing a Well-Architected ML workload.

2. The Machine Learning Lifecycle and the Well Architected Framework Pillars

This considers all aspects of the Machine Learning Lifecycle and reviews design strategies to align to pillars of the overall Well-Architected Framework.

  • The Machine Learning Lifecycle phases referenced in the ML Lens include:
    • Business goal identification – identification and prioritization of the business problem to be addressed, along with identifying the people, process, and technology changes that may be required to measure and deliver business value.
    • ML problem framing – translating the business problem into an analytical framing, i.e., characterizing the problem as an ML task, such as classification, regression, or clustering, and identifying the technical success metrics for the ML model.
    • Data processing – garnering and integrating datasets, along with necessary data transformations needed to produce a rich set of features.
    • Model development – iteratively training and tuning your model, and evaluating candidate solutions in terms of the success metrics as well as including wider considerations such as bias and explainability.
    • Model deployment – establishing the mechanism to flow data though the model in a production setting to make inferences based on production data.
    • Model monitoring – tracking the performance of the production model and the characteristics of the data used for inference.
  • The Well-Architected Framework Pillars are:
    • Operational Excellence – ability to support ongoing development, run operational workloads effectively, gain insight into your operations, and continuously improve supporting processes and procedures to deliver business value.
    • Security – ability to protect data, systems, and assets, and to take advantage of cloud technologies to improve your security.
    • Reliability – ability of a workload to perform its intended function correctly and consistently, and to automatically recover from failure situations.
    • Performance Efficiency – ability to use computing resources efficiently to meet system requirements, and to maintain that efficiency as system demand changes and technologies evolve.
    • Cost Optimization – ability to run systems to deliver business value at the lowest price point.
    • Sustainability – addresses the long-term environmental, economic, and societal impact of your business activities.

3. Cloud-agnostic best practices

These are best practices for each ML lifecycle phase across the Well-Architected Framework pillars irrespective of your technology setting. The best practices are accompanied by:

  • Implementation guidance – the AWS implementation plans for each best practice with references to AWS technologies and resources.
  • Resources – a set of links to AWS documents, blogs, videos, and code examples as supporting resources to the best practices and their implementation plans.

4. Indicative ML Lifecycle architecture diagrams to illustrate processes, technologies, and components that support many of these best practices.

What are the next steps?

The new Well-Architected Machine Learning Lens whitepaper is available now. Use the Lens whitepaper to determine that your ML workloads are architected with operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability in mind.

If you require support on the implementation or assessment of your Machine Learning workloads, please contact your AWS Solutions Architect or Account Representative.

Special thanks to everyone across the AWS Solution Architecture, AWS Professional Services, and Machine Learning communities, who contributed to the Lens. These contributions encompassed diverse perspectives, expertise, backgrounds, and experiences in developing the new AWS Well-Architected Machine Learning Lens.

The collective thoughts of the interwebz