AWS re:Invent 2018 — Machine Learning recap (Wednesday)

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.

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 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.

TensorFlow: near-linear scaling up to 256 GPUs.

Amazon SageMaker

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 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 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 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

New built-in algorithm for semantic segmentation

Inference Pipelines: a linear sequence of two to five containers that process requests for inferences on data

Built-in container for scikit-learn: you can train and host Scikit-learn models on Amazon SageMaker.

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

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.

Application Services

Amazon Personalize: a fully-managed service that puts personalization and recommendation in the hands of developers with little machine learning experience.

Amazon Forecast: a fully-managed deep learning service for time-series forecasting.

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.

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.