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: