Course Overview
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 Content
- Module 1: Big Data and Machine Learning on Google Cloud
- Module 2: Data Engineering for Streaming Data
- Module 3: Big Data with BigQuery
- Module 4: Machine Learning Options on Google Cloud
- Module 5: The Machine Learning Workflow with Vertex AI
- Module 6: Course Summary
Who should attend
This class is intended for the following:
- 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
Certifications
This course is part of the following Certifications:
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 Objectives
This course teaches participants the following skills:
- 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.
Follow On Courses
Outline: Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)
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.
- Lab: Exploring a BigQuery Public Dataset
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.
- Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
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.
- Lab: Predicting Visitor Purchases Using 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.
- Lab: Vertex AI: Predicting Loan Risk with AutoML
Module 5: Course Summary
This section reviews the topics covered in the course and provides additional resources for further learning.
Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.