Planning and Optimizing AI Infrastructure
Chapter 1: Foundations of AI Compute Infrastructure
Introduction to AI Workloads
The Role of CPUs in AI Systems
The Role of GPUs in Accelerating AI
Comparing CPU and GPU Architectures for ML
Introduction to TPUs and other ASICs
Memory and its Importance for Large Models
Storage Solutions for AI Datasets
Networking Considerations for Distributed Systems
Hands-on Practical: Benchmarking CPU vs GPU
Chapter 2: Designing On-Premise AI Infrastructure
Assessing Workload Requirements
Selecting Server Hardware for AI
GPU Interconnect Technologies
High-Speed Storage Configurations
Networking for Data and Model Transfer
Power and Cooling Requirements
Building a Bare-Metal AI Server
Practice: Creating a Hardware Specification Sheet
Chapter 3: Leveraging Cloud Platforms for AI
Overview of Major Cloud Providers for AI
Comparing Managed AI Services vs IaaS
Selecting Virtual Machine Instances for Training
Choosing Instances for Inference and Serving
Object Storage Services for Datasets
Understanding Cloud Networking and VPCs
Security Considerations in the Cloud
Hands-on Practical: Launching a GPU Cloud Instance
Chapter 4: Containerization and Orchestration for ML
Introduction to Docker for Reproducible Environments
Building a Docker Image with ML Libraries
Introduction to Kubernetes for Managing ML Workloads
Kubernetes Components: Pods, Services, Deployments
Managing GPU Resources in a Kubernetes Cluster
Using Kubeflow for ML Pipelines
Hands-on Practical: Deploying a Model on Kubernetes
Chapter 5: Strategies for Performance Optimization
Identifying Performance Bottlenecks
Techniques for Distributed Training
Using Mixed-Precision Training
Model Quantization for Efficient Inference
Optimizing Data Loading and Preprocessing Pipelines
Profiling GPU and CPU Usage
Hands-on Practical: Applying Mixed-Precision Training
Chapter 6: Cost Management and Optimization
Analyzing On-Premise Total Cost of Ownership
Understanding Cloud Pricing Models
Strategies for Reducing Cloud Compute Costs
Managing Data Storage and Transfer Costs
Implementing Cost Monitoring and Alerting
Right-Sizing Infrastructure for Workloads
Practice: Calculating and Comparing Job Costs
Security Considerations in the Cloud
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- AWS Shared Responsibility Model, Amazon Web Services (AWS), 2023 - Defines the fundamental security partnership between AWS and its customers, clarifying responsibilities for security of the cloud and security in the cloud.
- AWS Well-Architected Framework - Security Pillar, Amazon Web Services (AWS), 2023 - Offers architectural best practices for security in the cloud, covering identity, network, data protection, and other areas relevant to AI infrastructure.
- Best practices for IAM in your AWS account, Amazon Web Services (AWS), 2023 (Amazon Web Services) - Provides practical guidelines for implementing strong identity and access management, including the principle of least privilege and using IAM roles for secure application access.
- Guidelines on Security and Privacy in Public Cloud Computing, Peter Mell and Tim Grance, 2011 (National Institute of Standards and Technology (NIST)) - Offers vendor-neutral guidance on security and privacy in cloud computing, including data protection, identity management, and network security.
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