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.
• 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
• 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
Target Audience
This course is suitable for:
• 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
Optional certification available
