Tutorials and examples#

The following tutorials provide a hands-on introduction to using MLRun to implement a data science workflow and automate machine-learning operations (MLOps).

Make sure you start with the Quick start tutorial to understand the basics.

Click to view the tutorial

Each of the following tutorials is a dedicated Jupyter notebook. You can download them by clicking the download icon at the top of each page.

Train, Compare, and Register Models

Quick overview of training ML models using MLRun MLOps orchestration framework.

Serving ML/DL models

How to serve standard ML/DL models using MLRun Serving.

Projects and Automated ML Pipeline

How to work with projects, source control (git), and automating the ML pipeline.

Apply MLRun on Existing Code

Use MLRun to execute existing code on a remote cluster with experiment tracking.

Feature store example (stocks)

Build features with complex transformations in batch and serve in real-time.

Feature Store End-to-End Demo

Use the feature store with data ingestion, model training, model serving and automated pipeline.

You can find different end-to-end demos in MLRun demos repository at github.com/mlrun/demos.

See also:

Running the demos in Open Source MLRun#

By default, these demos work with the online feature store, which is currently not part of the Open Source MLRun default deployment:

  • fraud-prevention-feature-store

  • network-operations

  • azureml_demo