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Introduction to AI and Machine Learning on Google Cloud

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

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What's inside

Syllabus

Introduction

This module covers the course objective of helping learners navigate the AI development tools on Google Cloud. It also provides an overview of the course structure, which is based on a three-layer AI framework including AI foundations, development, and solutions.

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Builds a strong foundation in AI development on Google Cloud, which is standard in the industry

Taught by Google Cloud Training, who are recognized for their work in AI and machine learning

Develops skills in AI foundations, AI development, and AI solutions, which are core skills for data scientists, AI developers, and ML engineers

Examines generative AI, which is highly relevant to the latest advancements in AI

Explores the latest tools and technologies for AI development on Google Cloud

This course may require learners to have some prior knowledge of AI and machine learning

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Reviews summary

Foundational ai/ml on google cloud

According to learners, this course serves as a strong introduction to Google Cloud's AI and Machine Learning ecosystem. Many appreciate its comprehensive overview of services and tools, making it an ideal starting point for those new to AI/ML on GCP. The course is particularly praised for its inclusion of hands-on labs and coverage of cutting-edge topics like Generative AI. While it provides a solid foundational understanding, some advanced learners might find the depth insufficient for highly specialized roles.

Provides breadth, but some desire more in-depth content.

"The course covers a lot of ground, but sometimes I wished for a deeper dive into specific machine learning models or advanced techniques."

"It's a good overview, but if you're looking to become an expert in one specific area, you'll need to seek additional resources."

"I felt it touched on many topics briefly rather than going deep, which is fine for an introduction but leaves me wanting more."

Ideal for those new to GCP AI/ML, less for experts.

"This course is absolutely perfect for beginners. It doesn't assume much prior knowledge of AI/ML on GCP."

"I recommend this to anyone who wants to start their journey with AI/ML on Google Cloud. It lays a solid foundation."

"For intermediate or advanced users, you might find it a bit too introductory, but it's great for refreshing concepts or learning the GCP ecosystem."

Includes relevant and timely content on Gen AI.

"I was thrilled that the course included a module on Generative AI. It felt very current and relevant to today's landscape."

"The section on Gen AI Studio and Gemini multimodal was fantastic and very insightful for someone looking to get into modern AI."

"It’s great to see a course that quickly adapted to include generative AI, showing Google's commitment to staying updated."

Labs reinforce concepts and provide practical experience.

"The hands-on labs were incredibly helpful; actually using Vertex AI and BigQuery ML made the concepts stick."

"I enjoyed the practical exercises. They allowed me to get a feel for the tools and how they work in a real scenario."

"The lab sessions are the strongest part of the course for me, giving me confidence to explore further on GCP."

Provides a comprehensive and clear overview for beginners.

"This course offers an excellent high-level introduction to AI and ML on Google Cloud. It clarified many concepts for me."

"As someone new to GCP, I found this to be a really good starting point for understanding their AI/ML offerings."

"It's a perfect course for anyone looking for a foundational overview of AI and ML capabilities on Google Cloud."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to AI and Machine Learning on Google Cloud with these activities:

Review the prerequisites for the course

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Refreshing your knowledge of the prerequisites will ensure that you have a solid foundation before starting the course.

Browse courses on Machine Learning

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Review your notes or textbooks from previous courses
Complete any refresher materials provided by the instructor

Organize and review your notes, assignments, and quizzes

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Organizing your materials will help you prepare for assessments and retain the information better.

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Gather all of your notes, assignments, and quizzes
Sort and organize the materials by topic
Review the materials regularly

Review The Hundred-Page Machine Learning Book

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This book provides a solid overview of foundational concepts, including algorithms, models, and use cases.

View Machine Learning Engineering on Amazon

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Read one chapter per week
Complete the end-of-chapter exercises
Summarize the key concepts in your own words

Five other activities

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Follow a TensorFlow tutorial

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TensorFlow is one of the most popular deep learning libraries. Completing a tutorial will familiarize you with its API and core concepts.

Browse courses on TensorFlow

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Choose a tutorial that aligns with your skill level and interests
Follow the tutorial step-by-step
Experiment with different hyperparameters and model architectures

Solve LeetCode problems on data structures and algorithms

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Solving LeetCode problems will strengthen your programming skills and problem-solving abilities.

Browse courses on Data Structures

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Start with easy problems and gradually increase the difficulty
Analyze the problem and come up with an efficient solution
Implement and test your solution

Write a blog post or article on a topic related to the course

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Writing a blog post or article will help you organize your thoughts, reinforce your understanding, and solidify your knowledge.

Browse courses on Machine Learning

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Choose a topic that you are passionate about and knowledgeable in
Research and gather information from credible sources
Outline your article and write a draft
Edit and proofread your article carefully
Publish your article on a relevant platform

Mentor a junior student in machine learning

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Mentoring others will help you solidify your understanding of the concepts and develop your communication skills.

Browse courses on Mentoring

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Identify a junior student who is interested in learning about machine learning
Establish a regular meeting schedule
Discuss concepts and provide guidance on projects
Provide feedback and encouragement

Build a machine learning model and deploy it to a cloud platform

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Building and deploying a model will give you hands-on experience with the entire machine learning workflow.

Browse courses on Model Development

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Choose a dataset and define the problem you want to solve
Train and evaluate different machine learning models
Deploy the best model to a cloud platform
Monitor and evaluate the performance of your model

Career center

Learners who complete Introduction to AI and Machine Learning on Google Cloud will develop knowledge and skills that may be useful to these careers:

AI Researcher

AI Researchers push the boundaries of AI and ML by developing new algorithms, techniques, and applications. Their work contributes to advancements in areas such as natural language processing, computer vision, and robotics. This course provides AI Researchers with a solid foundation in AI and ML, empowering them to conduct cutting-edge research and contribute to the field.

See salaries and explore the career path for AI Researcher

Machine Learning Engineer

Machine Learning Engineers utilize AI and ML to develop intelligent systems that learn from vast amounts of data. Whether it's building predictive models, automating processes, or creating tailored customer experiences, Machine Learning Engineers solve complex problems with data-driven solutions. This course provides a comprehensive overview of ML development on Google Cloud, empowering learners to excel in this in-demand field.

See salaries and explore the career path for Machine Learning Engineer

AI Developer

AI Developers possess a deep understanding of AI algorithms and technologies, enabling them to design and implement AI systems. They use AI to automate processes, create intelligent applications, and solve real-world problems. This course helps AI Developers build a strong foundation in AI and ML, preparing them for success in this rapidly growing field.

See salaries and explore the career path for AI Developer

AI Product Manager

AI Product Managers oversee the development and launch of AI-powered products and services. They work closely with engineers, designers, and business stakeholders to ensure that AI solutions meet market needs and deliver value. This course equips AI Product Managers with a deep understanding of AI and ML, enabling them to make informed decisions and effectively manage AI product development.

See salaries and explore the career path for AI Product Manager

Data Scientist

Data Scientists research and develop techniques for extracting actionable insights from large datasets. Organizations leverage Data Scientists' expertise to understand their customers, improve personalized marketing, maintain and innovate products, and much more. This course provides a solid foundation in essential AI and ML topics, setting learners up for success in this data-driven field.

See salaries and explore the career path for Data Scientist

Cloud Architect

Cloud Architects design, develop, and manage scalable, reliable, and secure cloud computing solutions. They collaborate with other stakeholders to optimize performance, ensure data security, and align cloud strategies with business goals. This course provides Cloud Architects with a deep understanding of AI and ML on Google Cloud, empowering them to effectively incorporate AI into cloud solutions.

See salaries and explore the career path for Cloud Architect

Data Engineer

Data Engineers design, build, and maintain data pipelines that collect, transform, and store large volumes of data. Their expertise enables organizations to derive valuable insights from their data. This course equips Data Engineers with a solid foundation in AI and ML, enhancing their ability to develop and manage efficient data pipelines for AI applications.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts help organizations understand their business needs and develop solutions to improve operations. They leverage data analysis and technology to identify areas for improvement, streamline processes, and drive data-informed decision-making. This course provides Business Analysts with a strong understanding of AI and ML, empowering them to make data-driven recommendations and contribute to strategic planning.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts collect, analyze, and interpret data to extract meaningful insights. They use statistical techniques and tools to identify trends, patterns, and anomalies in data. This course may be useful for Data Analysts who want to leverage AI and ML to automate and enhance their data analysis processes, enabling them to derive more value from data.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a wide range of projects, from developing mobile apps to building enterprise systems. This course may be useful for Software Engineers who want to incorporate AI and ML into their software solutions, enhancing their capabilities and creating innovative applications.

See salaries and explore the career path for Software Engineer

Tech Lead

Tech Leads mentor and guide development teams, ensuring that projects are executed efficiently and effectively. Their technical expertise enables them to make informed decisions and resolve complex issues. This course may be useful for Tech Leads who want to enhance their understanding of AI and ML, enabling them to lead teams in developing innovative AI-powered solutions.

See salaries and explore the career path for Tech Lead

Product Owner

Product Owners define the vision and roadmap for software products. They work closely with development teams to ensure that products meet customer needs and deliver value. This course may be useful for Product Owners who want to incorporate AI and ML into their product strategies, enabling them to create innovative and competitive products.

See salaries and explore the career path for Product Owner

Data Architect

Data Architects design and manage data infrastructure and systems. They ensure that data is stored, processed, and accessed efficiently and securely. This course may be useful for Data Architects who want to leverage AI and ML to optimize data management and derive more value from data.

See salaries and explore the career path for Data Architect

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. They play a crucial role in risk management, portfolio optimization, and trading strategies. This course may be useful for Quantitative Analysts who seek to incorporate AI and ML into their financial models and enhance their analytical capabilities.

See salaries and explore the career path for Quantitative Analyst

Project Manager

Project Managers plan, execute, and deliver projects within defined constraints of scope, time, and budget. They coordinate with stakeholders, manage resources, and ensure that projects meet business objectives. This course may be useful for Project Managers who want to incorporate AI and ML into their project plans and leverage these technologies to enhance project outcomes.

See salaries and explore the career path for Project Manager

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to AI and Machine Learning on Google Cloud.

Deep Learning

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Classic introduction to deep learning, covering the fundamental concepts and algorithms. It must-read for anyone interested in learning about this field.

Introduction to AI and Machine Learning on Google Cloud

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Foundational ai/ml on google cloud

Activities

Career center

Reading list

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