Google Cloud Fundamentals: Big Data and Machine Learning

This 1-day 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.

Course Objectives

Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.

Design streaming pipelines with Dataflow and Pub/Sub.

Analyze big data at scale with BigQuery.

Identify different options to build machine learning solutions on Google Cloud.

Describe a machine learning workflow and the key steps with Vertex AI.

Build a machine learning pipeline using AutoML.

Audience

Data analysts, data scientists, and business analysts who are getting started with Google Cloud

Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports

Executives and IT decision-makers evaluating Google Cloud for use by data scientists

Prerequisites

Basic understanding of one or more of the following:

Database query language such as SQL

Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment

Machine learning models such as supervised versus unsupervised models

Course Outline

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Big Data and Machine Learning on Google Cloud

Identify the different aspects of Google Cloud’s infrastructure

Identify the big data and machine learning products on Google Cloud

Module 2: Data Engineering for Streaming Data

Describe an end-to-end streaming data workflow from ingestion to data visualization

Identify modern data pipeline challenges and how to solve them at scale with Dataflow

Build collaborative real-time dashboards with data visualization tools

Module 3: Big Data with BigQuery

Describe the essentials of BigQuery as a data warehouse

Explain how BigQuery processes queries and stores data

Define BigQuery ML project phases

Build a custom machine learning model with BigQuery ML

Module 4: Machine Learning Options on Google Cloud

Identify different options to build ML models on Google Cloud

Define Vertex AI and its major features and benefits

Describe AI solutions in both horizontal and vertical markets

Module 5: The Machine Learning Workflow with Vertex AI

Describe a ML workflow and the key steps

Identify the tools and products to support each stage

Build an end-to-end ML workflow using AutoML

Module 6: Summary

Recap of key learning points

Google Cloud Fundamentals: Big Data and Machine Learning, Virtual CDT