Machine Learning Lifecycle
Quickly run experiments with any ML library, framework, or language, locally or in the cloud, and on any platform.
Automatically keep track of parameters, results, code, and data from each experiment, and reproduce runs.
Interactively explore results in one place, and identify best performing models across multiple users.
Quickly deploy models to any platforms based on your needs, locally or in the cloud, from experimentation to production.
Run batch inference in near real-time and unmatched performance using Apache SparkTM.
Expose models via REST APIs with built-in integration with Docker containers, Azure ML, or Amazon SageMaker.