How Google Does Machine Learning
What are best practices for implementing machine learning on Google Cloud?
What you'll learn
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.
Table of contents
- Introduction 1m
- What is ML? 9m
- What kinds of problems can it solve? 6m
- Activity intro: Framing a machine learning problem 2m
- Activity: Framing a machine learning problem 0m
- Activity solutions: Framing a machine learning problem 4m
- Infuse your apps with ML 4m
- Build a data strategy around ML 10m
- Resources: What It Means to Be AI First 0m
- Introduction 1m
- Moving from experimentation to production 10m
- Components of Vertex AI 6m
- Pluralsight: Getting Started with GCP and Qwiklabs 4m
- Lab intro: Using an image dataset to train an AutoML model 0m
- Lab demo: Using an image dataset to train an AutoML model 7m
- Lab: Using an Image Dataset to Train an AutoML Model 0m
- Lab intro: Training an AutoML video classification model 0m
- Lab demo: Training an AutoML video classification model 9m
- Lab: Training an AutoML Video Classification Model 0m
- Tools to interact with Vertex AI 3m
- Resources: Machine Learning Development with Vertex AI 0m
- Introduction 0m
- Machine learning development with Vertex Notebooks 5m
- (Optional) Lab intro: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model 1m
- (Optional) Lab demo: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model 13m
- Lab: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model 0m
- Resources: Machine Learning Development with Vertex Notebooks 0m
