Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Pluralsight

How Google Does Machine Learning

via Pluralsight

This course may be unavailable.

Overview

What are best practices for implementing machine learning on Google Cloud?

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.

Taught by

Google Cloud

Reviews

Start your review of How Google Does Machine Learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.