Data Preparation provides a set of tools for efficiently exploring, understanding, and fixing problems in data. It allows you to consume data in many forms and transform that data into cleansed data that is better suited for downstream usage.
Experimentation service allows data scientists to execute their experiments locally, in Docker containers, or in Spark clusters through simple configuration. It manages run history, provides version control, and enables sharing and collaboration.
Model management enables data scientists to manage and deploy machine-learning workflows and models as containerized web services. It provides flexibility for on-prem, IoT edge, as well as cloud-based deployment. It also enables model versioning, telemetry tracking and more.