How Google Does Machine Learning - Locales

This course, How Google Does Machine Learning- Locales, is intended for non-English learners. If you want to take this course in English, please enroll in 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.

• Introduction to Course and Series
• Course series preview
• Course introduction
• What It Means to be AI-First
• Introduction
• What is ML?
• What problems can it solve?
• Activity intro: Framing a machine learning problem
• Lab solutions: Framing a machine learning problem
• Infuse your apps with ML
• Build a data strategy around ML
• Quiz: What it Means to be AI First
• How Google Does ML
• Introduction
• ML surprise
• The secret sauce
• ML and business processes
• The path to ML
• A closer look at the path to ML
• End of phases deep dive
• Quiz: How Google Does ML
• Machine Learning Development with Vertex AI
• Introduction
• Moving from experimentation to production
• Components of Vertex AI
• Lab intro: Using an image dataset to train an AutoML model
• Lab demo: Using an image dataset to train an AutoML model
• Using an Image Dataset to Train an AutoML Model
• Lab intro: Training an AutoML video classification model
• Lab demo: Training an AutoML video classification model
• Training an AutoML Video Classification Model
• Tools to interact with Vertex AI
• Quiz: Machine Learning Development with Vertex AI
• Machine Learning Development with Vertex Notebooks
• Introduction
• Machine learning development with Vertex Notebooks
• (Optional) Lab intro: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model
• (Optional) Lab demo: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model
• Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model
• Quiz: Machine Learning Development with Vertex Notebooks
• Best Practices for Implementing Machine Learning on Vertex AI
• Introduction
• Best practices for machine learning development
• Data preprocessing best practices
• Best practices for machine learning environment setup
• Quiz: Best Practices for Implementing Machine Learning on Vertex AI
• Responsible AI Development
• Introduction
• Overview
• Human biases lead to biases in ML models
• Biases in data
• Evaluating metrics with inclusion for your ML system
• Equality of opportunity
• How to find errors in your dataset using Facets
• Quiz: Responsible AI Development
• Summary
• Summary
• Resource: All quiz questions
• Resource: All readings
• Resource: All slides