Category: Amazon SageMaker

At re:Invent 2021, AWS announced several new Amazon SageMaker features that make machine learning (ML) accessible to new types of users while continuing to increase performance and reduce cost for data scientists and ML experts. In this post, we provide a summary of these announcements, along with resources for you to get more details on […]

In Part 1 of this series, we offered step-by-step guidance for creating, connecting, stopping, and debugging Amazon EMR clusters from Amazon SageMaker Studio in a single-account setup. In this post, we dive deep into how you can use the same functionality in certain enterprise-ready, multi-account setups. As described in the AWS Well-Architected Framework, separating workloads […]

February 2024: This blog post was reviewed and updated to include an updated AWS CloudFormation stack to comply with a recent Python3.7 lambda deprecation policy. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, […]

Machine learning operations (MLOps) are key to effectively transition from an experimentation phase to production. The practice provides you the ability to create a repeatable mechanism to build, train, deploy, and manage machine learning models. To quickly adopt MLOps, you often require capabilities that use your existing toolsets and expertise. Projects in Amazon SageMaker give […]

July 2024: This post was reviewed and updated for accuracy. Amazon Redshift, a fast, fully managed, widely used cloud data warehouse, natively integrates with Amazon SageMaker for machine learning (ML). Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Data analysts and database developers […]

Today we announce the general availability of Syne Tune, an open-source Python library for large-scale distributed hyperparameter and neural architecture optimization. It provides implementations of several state-of-the-art global optimizers, such as Bayesian optimization, Hyperband, and population-based training. Additionally, it supports constrained and multi-objective optimization, and allows you to bring your own global optimization algorithm. With […]

It’s almost here! Only 9 days until AWS re:Invent 2021, and we’re very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk […]

The AWS AI/ML Community has compiled a series of session guides to AWS re:Invent 2021 to help you get the most out of re:Invent this year. They covered four distinct categories relevant to AI/ML. With a number of our guide authors attending re:Invent virtually, you will find a balance between virtually accessible sessions and sessions […]

It is fourth-and-one on the Texans’ 36-yard line with 3:21 remaining on the clock in a tie game. Should the Colts’ head coach Frank Reich send out kicker Rodrigo Blankenship to attempt a 54-yard field goal or rely on his offense to convert a first down? Frank chose to go for it, leading to a […]

Amazon SageMaker Ground Truth supports many different types of labeling jobs, including several image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling. For situations not covered by these standard approaches, Ground Truth also supports custom image-based labeling, which allows you to create a labeling workflow with a completely unique UI and associated […]