Tag Archives: Amazon Sagemaker

FogHorn: Edge-to-Edge Communication and Deep Learning

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/foghorn-edge-to-edge-communication-and-deep-learning/

FogHorn is an intelligent Internet of Things ( IoT) edge solution that delivers data processing and real-time inference where data is created. Referring to itself as “the only ‘real’ edge intelligence solution in the market today,”  FogHorn is powered by a hyper-efficient Complex Event Processor (CEP) and delivers comprehensive data enrichment and real-time analytics on high volumes, varieties, and velocities of streaming sensor data, and is optimized for constrained compute footprints and limited connectivity.

Andrea Sabet, AWS Solutions Architect speaks with Ramya Ravichandar, Vice President of Products at Foghorn to talk about how FogHorn integrates with IoT MQTT for edge-to-edge communication as well as Amazon SageMaker for deep learning model deployment. The edgefication process involves running inference with real-time streaming data against a trained deep learning model. Drifts in the model accuracy trigger a callback to SageMaker for retraining.

*Check out more This Is My Architecture video series.

 

Architecture Monthly Magazine for July: Machine Learning

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/architecture-monthly-magazine-for-july-machine-learning/

Every month, AWS publishes the AWS Architecture Monthly Magazine (available for free on Kindle and Flipboard) that curates some of the best technical and video content from around AWS.

In the June edition, we offered several pieces of content related to Internet of Things (IoT). This month we’re talking about artificial intelligence (AI), namely machine learning.

Machine Learning: Let’s Get it Started

Alan Turing, the British mathematician whose life and work was documented in the movie The Imitation Game, was a pioneer of theoretical computer science and AI. He was the first to put forth the idea that machines can think.

Jump ahead 80 years to this month when researchers asked four-time World Poker Tour title holder Darren Elias to play Texas Hold’em with Pluribus, a poker-playing bot (actually, five of these bots were at the table). Pluribus learns by playing against itself over and over and remembering which strategies worked best. The bot became world-class-level poker player in a matter of days. Read about it in the journal Science.

If AI is making a machine more human, AI’s subset, machine learning, involves the techniques that allow these machines to make sense of the data we feed them. Machine learning is mimicking how humans learn, and Pluribus is actually learning from itself.

From self-driving cars, medical diagnostics, and facial recognition to our helpful (and sometimes nosy) pals Siri, Alexa, and Cortana, all these smart machines are constantly improving from the moment we unbox them. We humans are teaching the machines to think like us.

For July’s magazine, we assembled architectural best practices about machine learning from all over AWS, and we’ve made sure that a broad audience can appreciate it.

  • Interview: Mahendra Bairagi, Solutions Architect, Artificial Intelligence
  • Training: Getting in the Voice Mindset
  • Quick Start: Predictive Data Science with Amazon SageMaker and a Data Lake on AWS
  • Blog post: Amazon SageMaker Neo Helps Detect Objects and Classify Images on Edge Devices
  • Solution: Fraud Detection Using Machine Learning
  • Video: Viz.ai Uses Deep Learning to Analyze CT Scans and Save Lives
  • Whitepaper: Power Machine Learning at Scale

We hope you find this edition of Architecture Monthly useful, and we’d like your feedback. Please give us a star rating and your comments on Amazon. You can also reach out to [email protected] anytime. Check back in a month to discover what the August magazine will offer.