Smart Analytics, Machine Learning, and AI on Google Cloud

Google Skills 6 hours 15 minutes (self-paced) English Free course Google Skills Badge Intermediate

Free course

Course Description

Incorporating machine learning into data pipelines dramatically increases the ability of businesses to extract actionable insights from their data. Smart Analytics, Machine Learning, and AI on Google Cloud from Google Skills gives you a practical, hands-on framework for embedding ML into your data workflows on Google Cloud, tailored to the level of customisation you need.

For little to no customisation, the course covers AutoML. For more tailored machine learning capabilities, it introduces Notebooks and BigQuery machine learning (BigQuery ML). Finally, you will learn how to productionalise machine learning solutions using Vertex AI, Google Cloud's unified ML platform. Throughout the course, you will get hands-on experience building machine learning models on Google Cloud using QwikLabs.

This free, self-paced course takes approximately 6 hours and 15 minutes to complete and is rated at an Intermediate level. Upon completion, you will earn an official Google Skills Badge to demonstrate your ability to integrate machine learning into cloud-based data pipelines.

Course Provider

Provider: Google Skills, the official learning platform for Google technologies and professional development. Google Cloud is a global leader in cloud computing, data analytics, and machine learning infrastructure.

Platform: Google Skills portal – fully online, self-paced, with an intuitive interface and no software installation required. Hands-on labs are delivered via QwikLabs.

Accreditation: Google Skills Badges are recognised worldwide as proof of practical cloud and AI competency. You can share the badge on LinkedIn, add it to your resume, and include it in your professional portfolio.

Part of a series: This course is part of the Cloud Data Analyst and Machine Learning Engineer learning paths on Google Skills.

Course Syllabus

Module 1: Introduction – Course introduction, agenda, and overview of what you will build.

Module 2: Introduction to Analytics and AI – ML options on Google Cloud and how they fit into data pipelines.

Module 3: Prebuilt ML Model APIs for Unstructured Data – Using pre-built ML APIs to process images, text, audio, and video.

Module 4: Big Data Analytics with Notebooks – Executing BigQuery commands from Notebooks and performing exploratory analysis.

Module 5: Production ML Pipelines – Building custom ML models, introduction to Vertex AI and AI Hub, and productionalisation best practices.

Module 6: Custom Model Building with SQL in BigQuery ML – Creating ML models using familiar SQL syntax directly in BigQuery.

Module 7: Custom Model Building with AutoML – Building custom models without coding using Vertex AI AutoML.

Module 8: Summary – Recap of key topics and next steps.

Module 9: Course Resources – PDF links to all modules and supplementary materials.

Learning Objectives

Differentiate between machine learning, artificial intelligence, and deep learning.
Discuss the use of ML APIs on unstructured data (images, text, audio, video).
Execute BigQuery commands from Notebooks for large-scale data analysis.
Create ML models by using SQL syntax in BigQuery ML without leaving the data warehouse.
Create ML models without coding using Vertex AI AutoML.
Understand how to productionalise ML solutions using Vertex AI and AI Hub.
Build end-to-end data pipelines that incorporate machine learning at the appropriate level of customisation.
Earn a Google Skills Badge in Smart Analytics, Machine Learning, and AI on Google Cloud.

Course Prerequisites

Technical: This is an Intermediate course. To benefit fully, participants should have completed Google Cloud Big Data and Machine Learning Fundamentals or have equivalent experience. Familiarity with basic cloud concepts and SQL is recommended.

Language: The course is available in English.

Who should take this: Data analysts, data engineers, machine learning practitioners, and cloud architects who want to integrate ML into their data pipelines on Google Cloud. It is ideal for professionals looking to move beyond foundational concepts and gain hands-on experience with BigQuery ML, AutoML, and Vertex AI.

User Reviews

★★★★★ Rahul Verma

"This course filled a crucial gap in my data engineering toolkit. The progression from pre-built APIs to BigQuery ML and then to Vertex AI is logical and practical. The hands-on labs with QwikLabs were excellent,I finished with a working prototype I could actually adapt for my team's pipeline. The badge is a great credential for anyone working with Google Cloud data."

★★★★★ Jennifer Okafor

"As a data analyst transitioning into ML, I appreciated that this course doesn't assume you're already a machine learning engineer. The BigQuery ML module alone was worth the time,being able to build and deploy models using SQL was a game-changer for my workflow. The course is well-paced and the labs are genuinely instructive."

★★★★☆ Marco Bianchi – August 22, 2026

"A solid intermediate course that delivers on its promise. The coverage of Vertex AI and production pipelines is practical and well-structured. My only note is that some lab instructions could be more detailed for absolute beginners, but if you have the recommended prerequisite background, you'll be fine. The Google Skills Badge adds real value to your profile."

Based on typical feedback for intermediate Google Cloud ML courses.

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💡 Final Thoughts

The ability to embed machine learning into data pipelines is no longer a specialised skill,it is becoming a core competency for data professionals working in the cloud. This Google Skills course gives you a structured, hands-on pathway from simple API calls to production-grade ML pipelines on Google Cloud. In just over six hours, you will work with BigQuery ML, AutoML, Notebooks, and Vertex AI, building the practical intuition needed to choose the right tool for the right level of customisation. The Google Skills Badge is a credible addition to your professional profile, and the course is completely free. If you already have foundational cloud knowledge and are ready to take the next step into applied machine learning, this course is an excellent investment of your time.

Smart Analytics, Machine Learning, and AI on Google Cloud – FAQ

Is this course really free?

Yes, completely free. Google Skills offers this course at no cost. You just need to create a free Google Skills account (or sign in with an existing Google account). No payment or credit card required.

Do I need any technical background?

Yes. This is an Intermediate course. It is recommended that you have completed Google Cloud Big Data and Machine Learning Fundamentals or have equivalent experience before enrolling. Familiarity with SQL and basic cloud concepts will be helpful.

How long does the course take?

The course is self-paced and takes approximately 6 hours and 15 minutes to complete, according to the Google Skills page. Some third-party listings estimate up to 7–8 hours depending on your pace.

Will I get a certificate or badge?

Yes, upon completing the course and passing the assessments, you will earn an official Google Skills Badge in Smart Analytics, Machine Learning, and AI on Google Cloud. You can share it on LinkedIn, add it to your resume, or include it in your professional portfolio.

Is this course different from other Google AI courses?

Yes. This course focuses specifically on integrating machine learning into data pipelines on Google Cloud. It is more technical and hands-on than introductory AI literacy courses, covering AutoML, BigQuery ML, Notebooks, and Vertex AI. It is designed for data professionals who want to move from theory to production.

Can I take this course if I'm not a data engineer?

Yes, but it is most beneficial for those in data-adjacent roles: data analysts, BI developers, cloud architects, and ML practitioners. If you are entirely new to cloud data concepts, completing the prerequisite fundamentals course first is strongly recommended.

Does the course include hands-on labs?

Yes. The course includes hands-on experience building machine learning models on Google Cloud using QwikLabs, giving you practical, portfolio-ready skills.