Machine Learning Operations (MLOps)

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud.

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1 day course
Supporting material
Private
Private
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Course Credits

Select the pre-paid training investment that’s right for you and help your money stretch a little further with our course credits.

Jellyfish is an award-winning Google Cloud Partner. Our trainers work with Google Cloud on a daily basis, so you'll benefit from the years of industry experience they’ll share with you.

On this course, you'll get hands-on practice through a high-value, real-world use case: predictive customer life value, which uses BigQuery and TensorFlow workflows and then progresses toward training and deploying the model in the cloud with Vertex AI.

You'll learn everything you need to know about the MLOps concept and the considerations behind it, Machine Learning (ML) models and the concept of DevOps in ML, and the importance of operationalizing ML models in a unified AI platform, like Vertex AI.

By the end of the session, you'll have gained a comprehensive understanding of Vertex AI's MLOps capabilities.

This Machine Learning Operations (MLOps) course is available as a private session that can be delivered virtually or at a location of your choice in the US.

Course overview

Who should attend:

This course is ideal for aspiring machine learning data scientists, engineers, and analysts, customers, partners, and Googlers who want to streamline and automate their machine learning experimentation and operationalization workflows.

What you'll learn:

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

  • Understand (ML) models from an operational perspective
  • Understand the concept of DevOps in ML and the importance of operationalizing ML models in a unified AI platform, like Vertex AI
  • Understand the MLOps capabilities of Vertex AI
  • Understand how Vertex AI helps with the MLOps workflow

Prerequisites

In order to get the most out of this session, you should be proficient in Python; specifically when it comes to topics covered in the Crash Course on Python offered by Google. You should also have prior experience with foundational machine learning concepts and building machine learning solutions on Google Cloud - as covered in the Machine Learning on Google Cloud course.

Course agenda

Module 1: Welcome to the Machine Learning Operations (MLOps): Getting Started
  • Course Introduction
Module 2: Employing Machine Learning Operations
  • ML practitioners’ pain points
  • The concept of DevOps in ML
  • The three phases of the ML lifecycle
  • Automating the ML process
Module 3: Vertex AI & MLOps on Vertex AI
  • What is Vertex AI and why does a unified platform matter?
  • Introduction to MLOps on Vertex AI
  • How does Vertex AI help with the MLOps workflow?
Module 4: Summary
  • Review the core concepts covered in the course
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