Google Cloud Big Data and Machine Learning Fundamentals 日本語版

This module provides an overview of the course, introducing the reading list and setting the stage for the subsequent modules. Learners will gain insights into the foundational concepts necessary for the rest of the course.

This module delves into the realm of big data and machine learning in Google Cloud, covering computing, storage, product history, categories, and real-world examples. Additionally, it includes a lab for hands-on experience with BigQuery.

This module focuses on data engineering for streaming data, discussing challenges, message-driven architecture, Apache Beam for streaming pipeline design, Cloud Dataflow implementation, and visualization using Looker and Data Portal. It also features a lab for creating a real-time dashboard using Dataflow.

This module explores the use of BigQuery for big data, including storage and analysis, a demo of San Francisco bike share data, an overview of BigQuery ML, and hands-on experience with predicting visitor purchases using BigQuery ML.

Here, learners will discover the options available for machine learning in Google Cloud, including pre-built APIs, AutoML, custom training, and Vertex AI. This module provides a comprehensive understanding of the various approaches to machine learning in the Google Cloud environment.

With a focus on Vertex AI, this module guides learners through the machine learning workflow, covering data preparation, model training, evaluation, deployment, and monitoring. It also includes a lab for predicting loan risk using AutoML.

This module serves as a concluding summary of the course, encapsulating the key takeaways and providing a comprehensive wrap-up of the knowledge gained throughout the modules.