Category: Amazon SageMaker

Time series forecasting is a common problem in machine learning (ML) and statistics. Some common day-to-day use cases of time series forecasting involve predicting product sales, item demand, component supply, service tickets, and all as a function of time. More often than not, time series data follows a hierarchical aggregation structure. For example, in retail, […]

While the fuel economy of new gasoline or diesel-powered vehicles improves every year, green vehicles are considered even more environmentally friendly because they’re powered by alternative fuel or electricity. Hybrid electric vehicles (HEVs), battery only electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), hydrogen cars, and solar cars are all considered types of green vehicles. […]

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 […]

June 2023: 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 […]