Course
Skills Expanded
Google Cloud Big Data and Machine Learning Fundamentals
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle.
What you'll learn
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Table of contents
Course Introduction
6mins
Big Data and Machine Learning on Google Cloud
30mins
- Introduction 2m
- Compute 6m
- Storage 5m
- The history of big data and ML products 4m
- Big data and ML product categories 3m
- Customer example: Gojek 4m
- Pluralsight: Getting Started with GCP and Qwiklabs 4m
- Lab introduction: Exploring a BigQuery Public Dataset 1m
- Lab: Exploring a BigQuery Public Dataset 0m
- Summary 1m
- Reading list 0m
Data Engineering for Streaming Data
22mins
- Introduction 3m
- Big data challenges 2m
- Message-oriented architecture 5m
- Designing streaming pipelines with Apache Beam 2m
- Implementing streaming pipelines on Cloud Dataflow 4m
- Visualization with Looker 3m
- Visualization with Data Studio 1m
- Lab introduction: Creating a streaming data pipeline for a Real-Time dashboard with Dataflow 1m
- Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow 0m
- Summary 1m
- Reading list 0m
Big Data with BigQuery
39mins
- Introduction 6m
- Storage and analytics 4m
- BigQuery demo - San Francisco bike share 12m
- Introduction to BigQuery ML 4m
- Using BigQuery ML to predict customer lifetime value 5m
- BigQuery ML project phases 2m
- BigQuery ML key commands 3m
- Lab introduction: Predicting Visitor Purchases Using BigQuery ML 1m
- Lab: Predicting Visitor Purchases with a Classification Model with BigQuery ML 0m
- Summary 2m
- Reading list 0m
Machine Learning Options on Google Cloud
24mins
The Machine Learning Workflow with Vertex AI
27mins
Course Summary
4mins
