AI Infrastructure Essentials
This course provides a foundational overview of the hardware, software, and networking components required to develop and manage AI models at scale. It explores Google Cloud's AI Hypercomputer architecture, compares compute accelerators like GPUs and TPUs, and examines the critical data pipelines and storage solutions necessary to maximize training performance. It is designed for IT decision-makers and infrastructure architects seeking to understand enterprise-grade AI deployment.
- Duration: 3 hours
- Languages: English, German
- Level: Intermediate
- Group size: Up to 50
What you'll learn
- Differentiate between the layers of the AI Hypercomputer.
- Select appropriate accelerators for the most cost-effective AI workloads.
- Evaluate storage and networking solutions to maximize training goodput.
- Compare various deployment and consumption models for resource optimization.
Prerequisites
- Familiarity with cloud computing concepts.
- Understanding of general data center infrastructure.
Course outline
Foundations of AI infrastructure
- Definition of AI infrastructure
- The evolution of computing demands
- The need for new computing power
Google Cloud's AI Hypercomputer
- The AI Hypercomputer
- The 3 layers of the AI Hypercomputer: Overview
Compute accelerators: GPUs and TPUs
- Graphics Processing Units (GPU architecture, Google Cloud GPU family, Selecting GPUs)
- Tensor Processing Units (TPU architecture, Google Cloud TPU family, Best practices and considerations)
The AI data pipeline: Network and storage
- Maximizing goodput
- Networking for data ingestion and training
- Storage for data preparation and training
- Architecture for inference.
Orchestration and consumption
- Deployment options
- Flexible consumption
Course summary and quiz
- Course summary
- Q&A
- Quiz
Related courses
Agent Observability on Google Cloud
This course provides an applied, intermediate guide to operationalizing AI agents, focusing specifically on achieving production confidence and cost predictability for Gemini-powered workflows on Google Cloud. Participants will learn the methodology and actionable skills necessary to transform non-deterministic agent logic into transparent, auditable, and scalable systems.The course covers core operational disciplines, including mapping the agent's complex thought process (ReAct loops) to Cloud Trace Spans for debugging, implementing Logs-Based Security Metrics for compliance, and setting up actionable alerts and custom dashboards in Cloud Monitoring to proactively control cost overruns and quality drift. The course uses presentations, Visual Walkthroughs, and strategic discussions to ensure effective learning that is directly applicable to the Vertex AI ecosystem
Develop Search and Agents with AI Applications
In this course, you learn how to use Vertex AI Search and Conversational Agents to create search engines and chat applications. You will learn how to leverage Vertex AI Search for grounding your gen AI-powered applications. You will then explore how to integrate these search engines and chat applications into your own applications. Finally, you learn how to use the agents built in AI Applications in multiagent workflows.
Gemini in BigQuery for Data Practitioners
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.