Check out our guide to using TensorFlow Serving and how to orchestrate it with your ML pipeline

Bring your machine-learning applications to production

MLRun offers an integrative approach to manage your machine-learning pipelines from early development through management in your production environment. MLRun offers a convenient abstraction layer to a wide variety of technology stacks while empowering the Data Engineers and Data Scientists to define the feature and models. MLRun can be installed on Kubernetes with a few commands – start accelerating deployment of AI with MLOps today!

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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

Faster development to production through MLOps & Serverless automation

An Integrated Way to Accelerate Deployment of AI

Central metadata management, orchestration, and monitoring

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

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Data ingestion & preparation

Read and transform data from batch and online data stores

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Model training & testing

Train models at scale with automated testing functions

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Real-time data & model pipeline

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

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Data & model monitoring

Automate model monitoring and drift detection

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Elastic serverless runtimes & function marketplace

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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Start Using MLRun Now

Built-in Integrations

Enterprise MLRun

MLRun Resources

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