This learning path, designed for intermediate to advanced technical learners, offers on-demand courses to specialize in AI Infrastructure on Google Cloud. You'll gain the knowledge and skills to design and deploy high-performance AI/ML solutions using Google Cloud's AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine. Optimize every layer of your AI Infrastructure solutions, from storage and networking to orchestration and best practices, to redefine what's possible with Google AI Infrastructure.
Google Cloud AI Infrastructure Specialization
Google Cloud AI Infrastructure Specialization
Build and manage the infrastructure that powers AI. Build, deploy, and manage the Infrastructure for AI workloads.
Instructor: Google Cloud Training
Included with 
Get in-depth knowledge of a subject
(17 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
(17 reviews)
Intermediate level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Understand the core concepts of AI Hypercomputer architecture and its value.
Analyze the advantages and disadvantages of various deployment and provisioning options for AI infrastructure.
Select the optimal accelerator (GPU, TPU) for a given AI/ML workload.
Evaluate the performance and efficiency of AI workloads on different accelerators to maximize business value.
Overview
Skills you'll gain
- Artificial Intelligence
- Performance Improvement
- Computer Architecture
- MLOps (Machine Learning Operations)
- Cloud Infrastructure
- Cloud Computing Architecture
- Computer Hardware
- Systems Architecture
- Benchmarking
- Business Workflow Analysis
- Product Demonstration
- Cloud Platforms
- Interoperability
- Infrastructure Architecture
- Google Cloud Platform
- Application Deployment
- Hardware Architecture
- Performance Tuning
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
August 2025
3 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Google Cloud
Specialization - 3 course series
What you'll learn
Define the value and architecture of the AI Hypercomputer
Identify common use cases for using AI Hypercomputer
Explain how different types of accelerators (GPUs, TPUs, CPUs) contribute to the acceleration of AI training and inference.
Differentiate between various deployment options and choose the options that best suits your requirements.
Skills you'll gain
Performance Tuning, Systems Architecture, Cloud Infrastructure, Cloud Computing Architecture, Google Cloud Platform, Artificial Intelligence, Application Deployment, Infrastructure Architecture, Hardware Architecture, and Computer Architecture
What you'll learn
Define the value and architecture of Graphics Processing Units (GPUs)
Select the appropriate GPU machine type and provisioning platform to run your GPU-accelerated clusters
Explore various techniques for optimizing your GPU usage
Skills you'll gain
Computer Hardware, Cloud Infrastructure, Performance Tuning, Infrastructure Architecture, Artificial Intelligence, Hardware Architecture, Cloud Platforms, Infrastructure As A Service (IaaS), Cloud Computing Architecture, and Google Cloud Platform
What you'll learn
Discuss the advantages and disadvantages of using TPUs in different scenarios.
Choose the right TPU option to fit your specific needs
Implement strategies to maximize performance and efficiency when running business-related AI workloads on TPUs.
Explain the concept of GPU/TPU fungibility and its significance for creating flexible machine learning workflows.
Skills you'll gain
Interoperability, Google Cloud Platform, Cloud Computing Architecture, Benchmarking, Cloud Infrastructure, Tensorflow, Performance Improvement, Performance Tuning, Product Demonstration, and Business Workflow Analysis
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
Google Cloud Fundamentals: Core Infrastructure
Experience with Google Kubernetes Engine
High-Level AI/ML Concepts
1 hour per training, about 3 hours for the first three courses.
Although not necessary, it is recommended to take the courses in the specified order to maximize your learning. The content is structured sequentially, with each course building upon the foundational knowledge and skills introduced in the previous ones.
More questions
Financial aid available,