MLRun is an end-to-end open-source MLOps orchestration framework to manage and automate your entire analytics and machine learning lifecycle, from data ingestion, through model development, to full pipeline deployment in production and model monitoring. Its primary goal is to ease the development of machine learning pipeline at scale and enable faster deployment of fully operational ML products.
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 includes an online and offline feature store that handles the ingestion, processing, transformation, metadata, and storage of data and features. MLRun runs as a managed service in the Iguazio MLOps Platform.
In the following course you will learn how to:
- Set up an the open-source MLRun stack on a Kubernetes Cluster
- Train, test, and serve a Model using NLRun Serverless Functions
- Explore the different MLRun tutorials and demos
More information about MLRun and its architecture can be found here.