MLRun Package Documentation

The Open-Source MLOps Orchestration Framework.

Introduction

MLRun is an open-source MLOps framework that offers an integrative approach to managing your machine-learning pipelines from early development through model development to full pipeline deployment in production. MLRun offers a convenient abstraction layer to a wide variety of technology stacks while empowering data engineers and data scientists to define the feature and models.

MLRun Architecture

MLRun architecture

MLRun comprises of the following layers:

  • Feature & Artifact Store - Handle the ingestion, processing, metadata and storage of data and features across multiple repositories and technologies

  • Elastic Serverless Runtimes - Convert simple code to scalable and managed micro-services with workload specific runtime engines (Kubernetes jobs, Nuclio, Dask, Spark, Horovod, etc.) .

  • ML Pipeline Automation - Automated data preparation, model training & testing, deployment of production (real-time) pipelines, and end to end monitoring.

  • Central Management - Unified portal, UI, CLI, and SDK to manage the entire MLOps workflow which is accessible from everywhere.

Check the different documentation sections to learn about each of the components

Key Benefits

MLRun provides the following key benefits:

  • Rapid deployment of code to production pipelines

  • Elastic scaling of batch and real-time workloads

  • Feature management – ingestion, preparation, and monitoring

  • Works anywhere – your local IDE, multi-cloud, or on-prem

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