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. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.                                                                  

Identify 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.
• This course teaches participants the following skills:

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
• To get the most of out of this course, participants should have:

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
• This class is intended for the following: