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Bring your machine-learning applications to production

MLRun is an open MLOps framework for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements.

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Faster development to production through MLOps & Serverless automation

An Integrated Way to Accelerate Deployment of AI

Project Management and CI/CD Automation

Log all data, models and artifacts and track all code execution

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Ingest & Process

Read and transform data from batch and online data stores

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Train

Train models at scale with automated testing functions

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Deploy

Deploy real-time pipelines for data collection, model serving and monitoring

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Monitor

Automate model monitoring and drift detection

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Elastic serverless runtimes & Function Hub

Turn your Python code to composable functions, that can run at scale on Kubernetes, Dask, and Horovod in a single command. Leverage a function marketplace to accelerate your model development process

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Real-time and offline feature store & data connector

Deploy real-time pipelines for data collection, model serving and monitoring

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Rapid deployment

of code to production pipelines

Elastic scaling

for batch and real-time workloads

Feature management

ingestion, preparation, and monitoring

Works anywhere

in your local IDE, multi-cloud or on-prem

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Built-in Integrations

Enterprise MLRun

MLRun Resources

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