MLRun is the first end-to-end open source MLOps orchestration framework. MLRun offers an integrative approach to manage your machine-learning pipelines from early development to deployment to management in your production environment. It offers a convenient abstraction layer to a wide variety of technology stacks and empowers Data Engineers and Data Scientists to define the features and models, simplifying and accelerating the path to production.
Github | Join the Slack Community
of code to production pipelines
for batch and real-time workloads
ingestion, preparation, and monitoring
in your local IDE, multi-cloud or on-prem
Operators can painlessly set up the system through wizards, configure administration policies and register for system notifications, with no need for automation scripts or hands-on daily management. The Iguazio Data Science Platform with managed MLRun is delivered as an integrated offering with enterprise resiliency and functionality in mind. Enable data collaboration and governance across apps and business units without compromising security or performance. Authenticate and authorize users with LDAP integration and secure collaboration. The real-time data layer classifies data transactions with a built-in, data firewall that provides fine-grained policies to control access, service levels, multi-tenancy and data life cycles. Enterprise customers get dedicated 24/7 support to onboard, guide, and consult.
Achieve extreme performance with consistency at the lowest cost with the real-time data layer, built-in to enterprise-grade MLRun. With key-value and time series databases and an object store available out of the box, the data layer supports simultaneous, consistent and high-performance access through multiple industry standard APIs. The data layer provides fast, secure and shared access to real-time and historical data including NoSQL, SQL, time series and files. It runs as fast as in-memory databases on Flash memory, enabling lower costs and higher density.
Lift the weight of infrastructure management by leveraging built-in managed services for data analysis, ML/AI frameworks, development tools, dashboards, security and auth services, and logging. With managed MLRun, anyone on your ML team can simply choose a service, specify params and click deploy. Data scientists can work from Jupyter Notebooks or any other IDE and automatically turn it into an elastic and fully managed service directly from Jupyter or another IDE, with a single line of code.
