Machine learning news and insights

Automating complex deep learning model training using Amazon SageMaker Debugger and AWS Step Functions 2d

Amazon SageMaker Debugger can monitor ML model parameters, metrics, and computation resources as the model optimization is in progress. You can use it to identify issues during training, gain insights, and take actions like stopping the training or sending notifications through built-in or custom actions. Debugger is particularly useful in training challenging deep learning model […]

Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines 16d

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model […]

Automating Amazon Personalize solution using the AWS Step Functions Data Science SDK 21d

Machine learning (ML)-based recommender systems aren’t a new concept across organizations such as retail, media and entertainment, and education, but developing such a system can be a resource-intensive task—from data labelling, training and inference, to scaling. You also need to apply continuous integration, continuous deployment, and continuous training to your ML model, or MLOps. The […]

How to train procedurally generated game-like environments at scale with Amazon SageMaker RL 22d

A gym is a toolkit for developing and comparing reinforcement learning algorithms. Procgen Benchmark is a suite of 16 procedurally-generated gym environments designed to benchmark both sample efficiency and generalization in reinforcement learning.  These environments are associated with the paper Leveraging Procedural Generation to Benchmark Reinforcement Learning (citation). Compared to Gym Retro, these environments have […]

Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC 24d

Amazon SageMaker Studio notebooks provide a full-featured integrated development environment (IDE) for flexible machine learning (ML) experimentation and development. Security measures secure and support a versatile and collaborative environment. In some cases, such as to protect sensitive data or meet regulatory requirements, security protocols require that public internet access be disabled in the development environment. […]

Controlling and auditing data exploration activities with Amazon SageMaker Studio and AWS Lake Formation on 22 December

Highly-regulated industries, such as financial services, are often required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post walks you through the steps to implement access control and auditing capabilities on a per-user basis, using Amazon […]