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.
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
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
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
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