This module provides the overview of the course

Course introduction

Welcome to the Machine Learning Operations (MLOps): Getting Started

This module identifies ML practitioners' pain points before exploring the concept of DevOps in ML. You're introduced to the three phases of the ML lifecycle and automating the ML process.

Introduction to MLOps-Why and when to employ MLOps

Machine learning (ML) practitioners’ pain points

The concept of devOps in ML

ML lifecycle

Automating the ML process

Quiz Question 1

Quiz Question 2

Quiz Question 3

Quiz Question 4

Reading list

Employing Machine Learning Operations

This module explores what Vertex AI is and why a unified platform matters.

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, part 1?

How does Vertex AI help with the MLOps workflow, part 2?

Lab introduction: Vertex AI Pipelines: Qwik Start

ucase009-building-end-to-end-mlops-pipelines-using-vertex-ai

Building end-to-end MLOps pipelines using Vertex AI

Vertex AI and MLOps on Vertex AI

Summary

All Readings

Course Resources

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

Machine Learning Operations (MLOps): Getting Started

Learn MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. 

Reading list

Machine Learning Operations (MLOps): Getting Started

Exercițiu interactiv practic