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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:

• Part 1: MLRun Basics
  • Introduction to MLRun
  • Step 1: Installation and Setup
  • Step 2: Creating a Basic Function
  • Step 3: Running the Function on a Cluster
  • Step 4: Viewing Jobs on the Dashboard (UI)
  • Step 5: Scheduling Jobs
  • Done!
• Part 2: Training an ML Model
  • Prerequisites
  • Step 1: Setup and Configuration
  • Step 2: Creating a Training Function
  • Step 3: Exploring the Data with an MLRun Marketplace Function
  • Step 4: Testing Your Model
  • Done!
• Part 3: Serving an ML Model
  • Prerequisites
  • Step 1: Setup and Configuration
  • Step 2: Writing A Simple Serving Class
  • Step 3: Deploying the Model-Serving Function (Service)
  • Step 4: Using the Live Model-Serving Function
  • Step 5: Viewing the Nuclio Serving Function on the Dashboard
  • Done!
• Part 4: Projects and Automated ML Pipeline
  • Prerequisites
  • Step 1: Setting Up Your Project
  • Step 2: Updating Project and Function Definitions
  • Step 3: Defining and Saving a Pipeline Workflow
  • Step 4: Registering the Workflow
  • Step 5: Running A Pipeline
  • Step 6: Viewing the Pipeline on the Dashboard (UI)
  • Step 7: Invoking the Model
  • Done!

Quick-Start Guide Part 1: MLRun Basics