Tens of thousands of data scientists use Amazon SageMaker, because SageMaker makes it easy to solve business problems using machine learning (ML). SageMaker Studio provides a fully integrated development environment (IDE) for ML so you can prepare data, and build, train, and deploy models with a single, visual experience. Overall, data science teams can be up to 10x more productive using SageMaker.
Amazon SageMaker Ground Truth makes it helps you build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows including 3D point clouds, video, images, and text.
Low latency feature store
Amazon SageMaker Feature Store is a fully managed repository to store, update, retrieve, and share machine learning (ML) features. SageMaker Feature Store serves the exact same features in batch for training and in real-time for inference so you don’t need to write code to keep features consistent. You can easily add new features, update existing ones, retrieve features in batches for training, and get the same features with single digit millisecond latency for real-time inference.
Amazon SageMaker makes it possible to test and prototype locally. The Apache MXNet and TensorFlow Docker containers used in SageMaker are available on GitHub. You can download these containers to your local environment and use the SageMaker Python SDK to test your scripts before deploying to SageMaker training or hosting environments.
Amazon SageMaker supports reinforcement learning in addition to traditional supervised and unsupervised learning. SageMaker has built-in, fully-managed reinforcement learning algorithms, including some of the newest and best performing in the academic literature.
Managed spot training
Amazon SageMaker provides Managed Spot Training to help you to reduce training costs by up to 90%. This capability uses Amazon EC2 Spot instances, which is spare AWS compute capacity. Training jobs are automatically run when compute capacity becomes available and are made resilient to interruptions caused by changes in capacity, allowing you to save cost when you have flexibility with when to run training jobs.
Automatic model tuning
Amazon SageMaker can automatically tune your model by adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions the model is capable of producing saving weeks of effort. Automatic model tuning uses machine learning to quickly tune your model to be as accurate as possible.
Continuously monitor models
Amazon SageMaker Model Monitor automatically detects model and concept drifts and provides detailed alerts that help identify the source of the problem so you can improve model quality over time. All models trained in SageMaker automatically emit key metrics that can be collected and viewed in SageMaker Studio.
Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. Amazon Augmented AI provides built-in human review workflows for common machine learning use cases.
Amazon SageMaker Batch Transform eliminates the need to resize large datasets for batch processing jobs. Batch Transform allows you to run predictions on large or small batch datasets using a simple API.
Amazon SageMaker provides a scalable and cost effective way to deploy large numbers of custom machine learning models. SageMaker Multi-Model endpoints enable you to deploy multiple models with a single click on a single endpoint and serve them using a single serving container.