Category: Amazon SageMaker

Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects and files instead of databases and […]

AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications. The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can’t be deployed in the same AWS account where they are used. Examples of such […]

Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Previously, most methods were designed to separate MOT into […]

Computer vision algorithms are at the core of many deep learning applications. Self-driving cars, security systems, healthcare, logistics, and image processing all incorporate various aspects of computer vision. But despite their ubiquity, training computer vision algorithms, like Mask or Cascade RCNN, is hard. These models employ complex architectures, train on large datasets, and require computer […]

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 […]