MLflow provides a unified, scalable interface for deploying models as REST APIs that automatically adjust to meet demand fluctuations. With managed deployment on Databricks, your endpoints can intelligently scale up or down based on traffic patterns, optimizing both performance and infrastructure costs with no manual intervention required.

Deploy production models for batch inference directly on Apache Spark, enabling efficient processing of billions of predictions on massive datasets

Deploy models across multiple environments including Docker containers, cloud services like Databricks, Azure ML and AWS SageMaker, or Kubernetes clusters with consistent behavior.