Overview
This course introduces the fundamentals of Google Cloud's big data and machine learning solutions. It focuses on the data-to-AI lifecycle, showcasing how to use Google Cloud's infrastructure to design and implement data pipelines and build machine learning models. Learners will explore tools like BigQuery, Dataflow, Pub/Sub, Vertex AI, and AutoML, gaining hands-on experience through labs and real-world scenarios.
Prerequisites
To fully benefit from this course, learners should have:
- A basic understanding of database query languages such as SQL.
- Familiarity with data engineering workflows, including extract, transform, load (ETL) processes.
- A conceptual grasp of machine learning models, particularly supervised and unsupervised learning.
Target Audience
This course is designed for:
- Data analysts, data scientists, and business analysts beginning their journey with Google Cloud.
- Individuals responsible for data processing pipeline design, machine learning model development, and data visualization.
- Executives and IT decision-makers evaluating Google Cloud's big data and AI capabilities.
Learning Outcomes
By the end of this course, participants will be able to:
- Identify the data-to-AI lifecycle and major big data and machine learning products in Google Cloud.
- Design streaming data pipelines using Dataflow and Pub/Sub.
- Analyse large datasets at scale with BigQuery and create machine learning models using BigQuery ML.
- Explore various machine learning solutions on Google Cloud and implement workflows using Vertex AI and AutoML.
Course Outline
Module One: Introduction to big data and machine learning on Google Cloud
- Overview of Google Cloud's infrastructure and data-to-AI lifecycle.
- Key products supporting big data and AI.
- Activities:
- Lab: Exploring a BigQuery Public Dataset.
- Quiz.
Module Two: Data engineering for streaming data
- Managing streaming data with Pub/Sub and Dataflow.
- Creating data visualizations with Looker and Data Studio.
- Activities:
- Lab: Building a streaming data pipeline for real-time dashboard creation.
- Quiz.
Module Three: Big data analysis with BigQuery
- BigQuery essentials for large-scale data storage and processing.
- Introduction to BigQuery ML for custom machine learning models.
- Activities:
- Lab: Predicting visitor purchases using BigQuery ML.
- Quiz.
Module Four: Machine learning options on Google Cloud
- Overview of machine learning solutions available on Google Cloud.
- Introduction to Vertex AI for unified ML project management.
- Activities:
- Quiz.
Module Five: Machine learning workflow with Vertex AI
- Understanding the stages of a machine learning workflow: data preparation, model training, and deployment.
- Practical application using Vertex AI and AutoML.
- Activities:
- Lab: Predicting loan risk using AutoML with Vertex AI.
- Quiz.
Module Six: Course summary and additional resources
- Review of key concepts and tools.
- Guidance for continued learning in Google Cloud's big data and machine learning offerings.
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