How Google does Machine Learning

How Google does Machine Learning

This course is part of multiple programs.

Instructor: Google Cloud Training

What you'll learn

Describe Vertex AI Platform and how it's used to quickly build, train, and deploy AutoML machine learning models without writing any code
Describe best practices for implementing machine learning on Google Cloud
Leverage Google Cloud tools and environment to do ML
Articulate Responsible AI best practices

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 8 modules in this course

This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.

This module introduces the course series and the Google experts who will be teaching it.

What's included

2 videos

In this module, you explore building a data strategy around machine learning.

What's included

7 videos2 readings1 assignment

7 videosTotal 34 minutes

Introduction0 minutesPreview module
What is ML?8 minutes
What problems can it solve?6 minutes
Activity intro: Framing a machine learning problem1 minute
Activity solutions: Framing a machine learning problem4 minutes
Infuse your apps with ML3 minutes
Build a data strategy around ML10 minutes

2 readingsTotal 40 minutes

Activity: Framing a machine learning problem30 minutes
Resources: What It Means to Be AI First10 minutes

1 assignmentTotal 10 minutes

What it Means to be AI First10 minutes

This module shares the organizational know-how Google has acquired over the years.

What's included

7 videos1 reading1 assignment

All machine learning starts with some type of goal - whether it be a business use case, academic use case, or goal you are trying to solve. This module reviews the process of determining whether the model is ready for production the “proof of concept” or “experimentation” phase.

What's included

9 videos1 reading1 assignment2 app items

9 videosTotal 41 minutes

Introduction1 minutePreview module
Moving from experimentation to production9 minutes
Components of Vertex AI5 minutes
Getting Started with Google Cloud Platform and Qwiklabs4 minutes
Lab intro: Using an image dataset to train an AutoML model0 minutes
Lab demo: Using an image dataset to train an AutoML model7 minutes
Lab intro: Training an AutoML video classification model 0 minutes
Lab demo: Training an AutoML video classification model 9 minutes
Tools to interact with Vertex AI2 minutes

1 readingTotal 10 minutes

Resources: Machine Learning Development with Vertex AI10 minutes

1 assignmentTotal 14 minutes

Machine Learning Development with Vertex AI14 minutes

2 app itemsTotal 270 minutes

Lab: Using an image dataset to train an AutoML model120 minutes
Lab: Training an AutoML video classification model 150 minutes

This module explores both managed notebooks and user-managed notebooks for machine learning development in Vertex AI.

What's included

2 videos1 reading1 assignment

This module reviews best practices for a number of different machine learning processes in Vertex AI.

What's included

4 videos1 assignment

This module discusses why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.