Keeping track of new service and feature launches at re:Invent is pretty challenging, so here’s a quick recap on what happened today during Andy’s keynote.
Looking for yesterday’s announcements?
Infrastructure and Frameworks
Amazon Elastic Inference: a new service that lets you attach just the right amount of GPU-powered inference acceleration to any Amazon EC2 instance. This is also available for Amazon SageMaker notebook instances and endpoints, bringing acceleration to built-in algorithms and to deep learning environments.
Amazon Elastic Inference - GPU-Powered Deep Learning Inference Acceleration | Amazon Web Services
One of the reasons for the recent progress of Artificial Intelligence and Deep Learning is the fantastic computing…
AWS Inferentia: a machine learning inference chip designed to deliver high performance at low cost. AWS Inferentia will support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as well as models that use the ONNX format.
AWS Inferentia - Amazon Web Services (AWS)
AWS Inferentia provides high throughput, low latency inference performance at an extremely low cost. Each chip provides…
AWS DeepRacer: an 1/18th scale autonomous vehicle and a fully-configured cloud environment that you can use to train your Reinforcement Learning models. This takes advantage of the new Reinforcement Learning feature in Amazon SageMaker and also includes a 3D simulation environment powered by AWS RoboMaker.
AWS DeepRacer — Go Hands-On with Reinforcement Learning at re:Invent | Amazon Web Services
Reinforcement Learning is a type of machine learning that works when an “agent” is allowed to act on a trial-and-error…
TensorFlow: near-linear scaling up to 256 GPUs.
AWS-Optimized TensorFlow Now Scales to 256 GPUs
The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now support distributed training of TensorFlow deep learning…
Amazon SageMaker Ground Truth: a new capability of Amazon SageMaker that makes it easy for customers to to efficiently and accurately label the datasets required for training machine learning systems.
Amazon SageMaker Ground Truth - Build Highly Accurate Datasets and Reduce Labeling Costs by up to…
In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without…
Amazon SageMaker RL: a new capability of Amazon SageMaker extending its advantages to reinforcement learning, making it easier for all developers and data scientists regardless of their ML expertise.
Amazon SageMaker RL - Managed Reinforcement Learning with Amazon SageMaker | Amazon Web Services
In the last few years, machine learning (ML) has generated a lot of excitement. Indeed, from medical image analysis to…
Amazon SageMaker Neo: a new capability of Amazon SageMaker that enables machine learning models to train once and run anywhere in the cloud and at the edge with optimal performance.
Amazon SageMaker Neo - Train Your Machine Learning Models Once, Run Them Anywhere | Amazon Web…
Machine learning (ML) is split in two distinct phases: training and inference. Training deals with building the model…
Amazon SageMaker Search: a new capability that lets you find and evaluate the most relevant model training runs from the hundreds and thousands of your Amazon SageMaker model training jobs
Amazon SageMaker now comes with new capabilities for accelerating machine learning experimentation…
Data scientists and developers can now quickly and easily organize, track, and evaluate their machine learning (ML)…
New built-in algorithm for semantic segmentation
Semantic Segmentation algorithm is now available in Amazon SageMaker | Amazon Web Services
Amazon SageMaker is a managed and infinitely scalable machine learning (ML) platform. With this platform, it is easy to…
Inference Pipelines: a linear sequence of two to five containers that process requests for inferences on data
Amazon SageMaker Inference Pipelines - Amazon SageMaker
Use inference pipelines in Amazon SageMaker for real-time and batch transform requests.
Built-in container for scikit-learn: you can train and host Scikit-learn models on Amazon SageMaker.
A library for training and deploying machine learning models on Amazon SageMaker - aws/sagemaker-python-sdk
Git integration: associating GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter Notebooks
Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration…
It's now possible to associate GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker…
Machine Learning models in the AWS Marketplace: a new Machine Learning category of products offered by AWS Marketplace, which includes over 150+ algorithms and model packages, with more coming every day.
NEW - Machine Learning algorithms and model packages now available in AWS Marketplace | Amazon Web…
At AWS, our mission is to put machine learning in the hands of every developer. That's why in 2017 we launched . Since…
Amazon Personalize: a fully-managed service that puts personalization and recommendation in the hands of developers with little machine learning experience.
Amazon Personalize - Real-Time Personalization and Recommendation for Everyone | Amazon Web…
Machine learning definitely offers a wide range of exciting topics to work on, but there's nothing quite like…
Amazon Forecast: a fully-managed deep learning service for time-series forecasting.
Amazon Forecast - Time Series Forecasting Made Easy | Amazon Web Services
The capacity to foresee the future would be an incredible superpower. At AWS, we can't give you that, but we can help…
Amazon Textract: a new fully-managed service that automatically extracts text and data from scanned documents. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.
Amazon Textract | Extract Text & Data | AWS
Easily extract text and data from virtually any document using Amazon Textract. Textract goes beyond simple optical…
Now it’s time to read about these and try them out! Happy to answer questions! Please follow me on Twitter for more live news and content.