How Google does Machine Learning

What are best practices for implementing machine learning on Google Cloud? What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code? What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about providing a unified platform for managed datasets, a feature store, a way to build, train, and deploy machine learning models without writing a single line of code, providing the ability to label data, create Workbench notebooks using frameworks such as TensorFlow, SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability to train custom models, build component pipelines, and perform both online and batch predictions. We also discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important to not skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them.

• Describe Vertex AI Platform and how it's used to quickly build, train,and deploy AutoML machine learning models without writing a single line of code

• Describe best practices for implementing machine learning on Google Cloud

• Leverage Google Cloud Platform tools and environment to do ML

• Articulate Responsible AI best practices

Syllabus

Syllabus - What you will learn from this course
Week 1
Introduction to Course and Series
What It Means to be AI-First
How Google Does ML
Week 2
Machine Learning Development with Vertex AI
Week 3
Machine Learning Development with Vertex Notebooks
Best Practices for Implementing Machine Learning on Vertex AI
Responsible AI Development
Summary

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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Last Updated: September 1, 2015

Reviews

Machine Learning API's were really cool stuff to learn and see the examples running.

Though a little more emphasis is needed to understand the codes used in the final lab session :)

The course is packed with wonderful learning resources. However I think that the course should be further divided into subsequent weeks so that it is easy to keep the track of the course.

The course covers lots of best practices when transferring an organization into a ML organization. It's not too technical but covers high-level concepts and know-how. I really like it

I actually learnt lot of good stuff regarding GCP and ML on cloud. This is the beginner course for anyone who wants to start off their career in GCP and ML. Thanks coursera and GCP for this course