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.

The MLRun Architecture

MLRun architecture

MLRun is composed of the following layers:

  • Feature and Artifact Store

    handles the ingestion, processing, metadata, and storage of data and features across multiple repositories and technologies.

  • Elastic Serverless Runtimes

    converts simple code to scalable and managed microservices with workload-specific runtime engines (such as Kubernetes jobs, Nuclio, Dask, Spark, and Horovod).

  • ML Pipeline Automation

    automates data preparation, model training and testing, deployment of real-time production pipelines, and end-to-end monitoring.

  • Central Management

    provides a unified portal for managing the entire MLOps workflow. The portal includes a UI, a CLI, and an SDK, which are accessible from anywhere.

Review the relevant documentation sections to learn about each component.

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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