Getting-Started Tutorial

This tutorial provides a hands-on introduction to using MLRun to implement a data science workflow and automate machine-learning operations (MLOps).

The tutorial covers MLRun fundamentals such as creation of projects and data ingestion and preparation, and demonstrates how to create an end-to-end machine-learning (ML) pipeline. MLRun is integrated as a default (pre-deployed) shared service in the Iguazio Data Science Platform.

You’ll learn how to

  • Collect (ingest), prepare, and analyze data

  • Train, deploy, and monitor an ML model

  • Create and run an automated ML pipeline

You’ll also learn about the basic concepts, components, and APIs that allow you to perform these tasks, including

  • Setting up MLRun

  • Creating and working with projects

  • Creating, deploying and running MLRun functions

  • Using MLRun to run functions, jobs, and full workflows

  • Deploying a model to a serving layer using serverless functions

The tutorial is divided into four parts, each with a dedicated Jupyter notebook. The notebooks are designed to be run sequentially, as each notebook relies on the execution of the previous notebook: