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.

• Course Introduction
• This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.
• Big Data and Machine Learning on Google Cloud
• This section explores the key components of Google Cloud's infrastructure. It's here that we introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
• Data Engineering for Streaming Data
• This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Looker Studio.
• Big Data with BigQuery
• This section introduces learners to BigQuery, Google's fully-managed, serverless data warehouse. It also explores BigQuery ML, and the processes and key commands that are used to build custom machine learning models.
• Machine Learning Options on Google Cloud
• This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.
• The Machine Learning Workflow with Vertex AI
• This section focuses on the three key phases--data preparation, model training, and model preparation--of the machine learning workflow in Vertex AI. Learners get the opportunity to practice building a machine learning model with AutoML.
• Course Summary
• This section reviews the topics covered in the course, and provides additional resources for further learning.