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.
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
- Resources