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 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