Category: Amazon SageMaker

Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than […]

Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of […]

Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and […]

July 2023: You can also use the newly launched JumpStart APIs, an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. Please refer to Amazon SageMaker JumpStart models and algorithms now available via API for more details on how […]

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE, and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) […]

Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model. In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast. The benefits of Hyperband Hyperband presents two advantages over […]

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML […]

“85% of buyers trust online reviews as much as a personal recommendation” – Gartner Consumers are increasingly engaging with businesses through digital surfaces and multiple touchpoints. Statistics show that the majority of shoppers use reviews to determine what products to buy and which services to use. As per Spiegel Research Centre, the purchase likelihood for […]

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, including preparing data and building, training, and deploying models. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and […]

This post is co-written with Rajnish Jain, Priyanka Kulkarni and Daniel Johnson from Medidata. Medidata is leading the digital transformation of life sciences, creating hope for millions of patients. Medidata helps generate the evidence and insights to help pharmaceutical, biotech, medical devices, and diagnostics companies as well as academic researchers with accelerating value, minimizing risk, […]