ABOUT

ML teams struggle to move from notebook experiments to production because there is no unified platform that handles data preparation, feature computation, model training, deployment, and monitoring as a cohesive workflow. MLRun solves this by providing a serverless MLOps framework that orchestrates the entire ML lifecycle on Kubernetes. It handles automatic ingestion of data sources, real-time and batch feature computation through its integrated feature store, distributed training job submission, model packaging with versioning, automated deployment to serverless or Kubernetes serving runtimes, and real-time model monitoring with drift detection — all through a unified Python API.

INTEGRATION GUIDE

1. Build end-to-end ML pipelines that automate data ingestion, feature engineering, model training, and deployment in a single serverless workflow 2. Manage online and offline feature computation through an integrated feature store that serves both training and inference with consistent transformations 3. Deploy ML models to production with one command, automatically packaging them into serverless functions or Kubernetes deployments with auto-scaling 4. Monitor production model performance with automated drift detection, data quality alerts, and real-time prediction logging 5. Run distributed hyperparameter tuning and training jobs on Kubernetes clusters with automatic resource management and parallel execution

MLRUN

ABOUT

INTEGRATION GUIDE

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