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!

Latest News
(19. Jul 2021)
(13. Jul 2021)
(6. Jun 2021)
media-columns-repeater__item-wrap

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

Read More

Data ingestion & preparation

Read and transform data from batch and online data stores

Read More

Model training & testing

Train models at scale with automated testing functions

Read More

Real-time data & model pipeline

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

Read More

Data & model monitoring

Automate model monitoring and drift detection

Read More

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

Read More

Real-time and offline feature store & data connector

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

Read More

Start Using MLRun Now

Built-in Integrations

Enterprise MLRun

MLRun Resources

Read More