Amazon SageMaker is a fully managed service that enables users to build, train, and deploy machine learning models for various use cases. It offers a range of tools and workflows to streamline the ML development process and improve team productivity.

Features:

• A variety of tools for data scientists, business analysts, and ML engineers.
• Access to large amounts of structured and unstructured data for ML.
• Optimized infrastructure for reduced training time from hours to minutes.
• Increased team productivity by up to 10 times with purpose-built tools.
• Automated and standardized MLOps practices and governance for transparency and auditability.

Use Cases:

• Business analysts can use SageMaker Canvas to make ML predictions.
• Data scientists can use SageMaker Studio to prepare data, build, train, and deploy models.
• ML engineers can use SageMaker MLOps to deploy and manage models at scale.

Amazon SageMaker supports popular ML frameworks, toolkits, and programming languages, and is based on Amazon's extensive experience in developing real-world ML applications.