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

Introduction to MLRun - Use serverless functions to train and deploy models

Gen AI tutorials#

Deploy LLM using MLRun

How to copy a dataset into your cluster, deploy an LLM in the cluster, and run your function.

Model monitoring using LLM

Set up an effective model monitoring system that leverages LLMs to maintain high standards for deployed models.

Machine learning tutorials#

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

Serving pre-trained ML/DL models

How to deploy real-time serving pipelines with MLRun Serving and different types of pre-trained ML/DL models.

Projects & automated ML pipeline

How to work with projects, source control (git), CI/CD, to easily build and deploy multi-stage ML pipelines.

Real-time monitoring & drift detection

Demonstrate MLRun Serving pipelines, MLRun model monitoring, and automated drift detection.

Add MLOps to existing code

Turn a Kaggle research notebook to a production ML micro-service with minimal code changes using MLRun.

Basic feature store example (stocks)

Understand MLRun feature store with a simple example: build, transform, and serve features in batch and in real-time.

Batch inference and drift detection

Use MLRun batch inference function (from MLRun Function Hub), run it as a batch job, and generate drift reports.

Advanced real-time pipeline

Demonstrates a multi-step online pipeline with data prep, ensemble, model serving, and post processing.

Feature store end-to-end demo

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

End to end demos#

See Demos.

Cheat sheet#

If you already know the basics, use the cheat sheet as a guide to typical use cases and their flows/SDK.