Machine Learning Integration into Data Pipelines on Google Cloud

Course Description

This course explores various methods of integrating machine learning into data pipelines within the Google Cloud infrastructure. With a hands-on approach, the course covers the use of AutoML for minimal customization, Notebooks, and BigQuery ML for more tailored solutions, and delves into deploying machine learning models into production using Vertex AI.

What Students Will Learn

  • Understanding the distinctions between ML, AI, and Deep Learning.
  • Utilizing ML APIs to process unstructured data.
  • Executing BigQuery commands through Notebooks.
  • Creating machine learning models using SQL syntax in BigQuery.
  • Building ML models using AutoML with minimal coding.

Prerequisites

Students should have completed the “Google Cloud Big Data and Machine Learning Fundamentals”, or possess equivalent practical experience in order to fully benefit from this course.

What the Course Will Cover

  • Integration of AutoML, Notebooks, and BigQuery ML into data pipelines.
  • Deployment of machine learning models using Vertex AI.
  • Hands-on labs and projects through QwikLabs for practical experience.

Who This Course Is For

This course is designed for individuals who are interested in advancing their knowledge and skills in machine learning and its integration into data analytics pipelines, particularly within the Google Cloud ecosystem.

How Learners Can Use These Skills in the Real World

Learners will be able to efficiently extract actionable insights from large data sets by employing advanced ML tools available on Google Cloud, streamline the process of model training and deployment, and effectively handle complex data analysis tasks in a cloud environment.

Syllabus

  • 1. Introduction to the course and agenda.
  • 2. Overview of ML options on Google Cloud.
  • 3. Using pre-built ML APIs for unstructured data.
  • 4. Data analytics using Notebooks.
  • 5. Building and deploying production ML pipelines.
  • 6. Building custom ML models using SQL in BigQuery ML.
  • 7. Building models with AutoML.
  • 8. Course summary and recap.
  • 9. Access to PDF resources for all modules.