Google Cloud Big Data and Machine Learning Fundamentals Duration: 1 Day

Course details

Module 1: Course Introduction

• Meet the author
• Course introduction

Module 2: Big Data and Machine Learning on Google Cloud

• Introduction
• Google Cloud infrastructure
• Compute
• Storage
• The history of big data and ML products
• Big data and ML product categories
• Customer example: Gojek
• Lab introduction: Exploring a BigQuery Public Dataset
• Lab: Exploring a BigQuery Public Dataset
• Summary
• Quiz
• Document: Reading list

Module 3: Data Engineering for Streaming Data

• Introduction
• Big data challenges
• Message-oriented architecture
• Designing streaming pipelines with Apache Beam
• Implementing streaming pipelines on Cloud Dataflow
• Visualization with Looker
• Visualization with Looker Studio
• Lab introduction: Creating a streaming data pipeline for a Real-Time dashboard with Dataflow
• Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
• Summary
• Quiz

Module 4: Big Data with BigQuery

• Introduction
• Storage and analytics
• Demo: Querying TB of data in seconds
• Introduction to BigQuery ML
• Using BigQuery ML to predict customer lifetime value
• BigQuery ML project phases
• BigQuery ML key commands
• Lab introduction: Predicting Visitor Purchases Using BigQuery ML
• Lab: Predicting Visitor Purchases with BigQuery ML
• Summary
• Quiz

Module 5: Machine Learning Options on Google Cloud

• Introduction
• Options to build ML models
• Pre-built APIs
• AutoML
• Custom training
• Vertex AI
• AI Solutions
• Summary
• Quiz

Module 6: The Machine Learning Workflow with Vertex AI

• Introduction
• Data preparation
• Model training
• Model evaluation
• Model deployment and monitoring
• Lab introduction: Predicting loan risk with AutoML
• Lab: Vertex AI: Predicting Loan Risk with AutoML
• Lab recap: Predicting loan risk with AutoML
• Summary
• Quiz

Module 7: Course Summary

• Course Summary