MLRun simplifies & accelerates the production pipeline design using a modular strategy, where the different parts contribute to a continuous, automated, and far simpler path from research and development to scalable production pipelines, without refactoring code, adding glue logic, or spending significant efforts on data and ML engineering.
MLRun uses Serverless Function technology: write the code once, using your preferred development environment and simple “local” semantics, and then run it as-is on different platforms and at scale. MLRun automates the data processing and movement, build process, execution, scaling, versioning, parameterization, outputs tracking, CI/CD integration, deployment to production, monitoring, and more.
MLRun backend service can run locally or over Kubernetes (preferred), see the instructions for installing it locally using Docker or over Kubernetes Cluster. Alternatively, you can use Iguazio's managed MLRun service.
This Jupyter Notebook server is designed to work with the different options, it works out of the box with the local deployment mode.
In order to work with remote MLRun service (Kubernetes or managed) you need to edit and save the mlrun.env file in the following way:
# set remote MLRun service address, username and access-key
MLRUN_DBPATH=https://<service-address>
V3IO_USERNAME=<user>
V3IO_ACCESS_KEY=<access-key>Once You are done, save the file using the menu or ctrl + s, you may need to restart existing Notebooks for the changes to take effect.
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 before going through the other notebooks.
- Quick Start Tutorial
- Train, Compare, and Register Models
- Serving ML/DL models
- Projects and Automated ML Pipeline
You can find different end to end demos in under the demos folder.
Or go through interactive MLRun Katacoda Scenarios which teach how to install and use MLRun.