MLRun is an end-to-end open-source MLOps solution to manage and automate your entire analytics and machine learning lifecycle, from data ingestion, 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 features and models with ease and flexibility.
MLRun is running as a built-in service in the Iguazio Data Science Platform and is deeply integrated with other services in the platform. Its primary goal is to ease the development of machine learning pipeline at scale and help organizations build a robust process for moving ML projects from the research phase to fully operational production deployments.
MLRun serving capabilities can take MLRun models or standard model files and produce managed, real-time, serverless functions based on the Nuclio real-time serverless framework in complex and flexible pipelines. Nuclio is focused on performance and flexibility, and is built around data, I/O, and compute intensive workloads.
In the following course you will learn how to:
- Set up an MLRun stack on a Kubernetes Cluster
- Train, test, and serve a Model
More information about MLRun and its architecture can be found here.