Getting Started With JFrog ML

Welcome to Getting Started With JFrog ML!

This course is designed to introduce you to the JFrog ML platform and guide you through the essential first steps of building and managing your machine learning models. We'll explore why treating models as critical assets is crucial in today's AI-driven landscape and how JFrog ML helps bridge the gap between machine learning workflows and standard DevOps/DevSecOps practices.

Key Topics Covered:

• Introduction to JFrog ML:
  • The importance of treating models as critical assets.
  • Integrating ML into DevOps/DevSecOps.
  • Unique aspects of MLOps compared to traditional DevOps.
  • Overview of the JFrog ML platform and its role in the ML lifecycle.
• Getting Started with Your First Model Build:
  • How JFrog ML fits within the broader JFrog ecosystem (Artifactory, Xray).
  • Installing the JFrog ML SDK.
  • Configuring the JFrog ML CLI.
• Defining and Building Your First JFrog ML Model:
  • Understanding the JFrog ML Model SDK (build and predict functions).
  • Implementing training logic in the build function.
  • Serving inference with the predict function.
  • Initiating a model build via the CLI.
• Local Testing and Monitoring the Remote Build:
  • Utilizing local model testing for faster debugging.
  • Monitoring model builds in the JFrog ML console.
  • Examining build details, including code snapshots and Xray security scans.

By the end of this course, you will be able to:

• Explain the value of an end-to-end MLOps platform like JFrog ML.
• Set up your development environment with the JFrog ML SDK and CLI.
• Define the structure of an ML model using the JFrog ML Model SDK, including its build and predict functions.
• Initiate and monitor a remote model training process on the JFrog ML platform.
• Perform local testing of your ML model.
• Navigate the JFrog ML console to view build details and understand security scan results.

Enjoy learning!