Advanced AI Infrastructure Design and Optimization
Chapter 1: Chapter 1: Architectural Patterns for AI Platforms
MLOps Principles at Scale
Compute Selection: CPU, GPU, and TPU Architectures
High-Bandwidth Interconnects for Distributed Systems
Storage Solutions for Large-Scale AI Datasets
Networking Topologies for ML Clusters
Hands-on Practical: Environment and Tooling Setup
Chapter 2: Chapter 2: Engineering Distributed Model Training
Data Parallelism with Synchronous and Asynchronous Updates
Model and Pipeline Parallelism for Large Models
Implementing Training with Horovod
Leveraging Microsoft DeepSpeed for ZeRO and Offloading
Fault Tolerance and Checkpointing in Long-Running Jobs
Hands-on Practical: Distributed Training with PyTorch FSDP
Chapter 3: Chapter 3: Advanced Resource Orchestration with Kubernetes
Managing ML Workflows with KubeFlow Pipelines
Advanced GPU Scheduling and Sharing
Cluster Autoscaling for Dynamic ML Workloads
Strategies for Using Spot and Preemptible Instances
Multi-Tenancy with Namespaces, Quotas, and Priority Classes
Practice: Configure a GPU-Aware Autoscaling Group
Chapter 4: Chapter 4: High-Performance Model Inference and Serving
Architecting Inference Services for Latency and Throughput
Model Optimization with TensorRT and ONNX Runtime
Model Quantization Techniques: INT8 and FP8
Serving Multiple Models with NVIDIA Triton Inference Server
A/B Testing and Canary Deployments for Models
Hands-on Practical: Deploying an Optimized Model on Triton
Chapter 5: Chapter 5: Scalable Data Management and Feature Engineering
Designing and Implementing a Feature Store
Real-time vs. Batch Feature Computation
Data Versioning and Lineage with DVC and Pachyderm
High-Throughput Data Processing with Spark and Ray
Managing Data Lakes and Data Warehouses for AI
Practice: Build a Basic Feature Ingestion Pipeline
Chapter 6: Chapter 6: Financial Operations and Governance for AI
Applying FinOps Principles to ML Workloads
Cost Attribution and Showback Models for ML Teams
Optimizing Cloud Storage Costs for Datasets
Right-Sizing Compute for Training and Inference
Automating Cost Anomaly Detection
Governance Policies for Resource Consumption
Practice: Analyzing a Cloud Cost and Usage Report
Compute Selection: CPU, GPU, and TPU Architectures
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- In-Datacenter Performance Analysis of a Tensor Processing Unit, Norman P. Jouppi, Cliff Young, David Patil, Dustin Patterson, Gaurav Agrawal, Haixin Liao, Kevin Schardl, Mike Smith, Dave Washington, Zhifeng Chen, Yuan Cao, Branden Holt, Luke Taylor, Alan Knaggs, Rohan Kumar, Sanjay Nigam, Guang Gao, Al Gleason, Robert Horton, Daniel Kini, Danny Kwon, David Leyda, Francois Obermeyer, Rajkumar Rathod, Kevin Ren, Aditya Taraporevala, Grant Vert, Xinan Long, Niels Dekker, Andy Hölzle, Quoc Le, Chengyu Sun, 2017 Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA '17) (ACM) DOI: 10.1145/3079893.3080246 - Describes the architecture and performance characteristics of Google's first-generation Tensor Processing Unit (TPU).
- NVIDIA Volta GV100 GPU Architecture, David B. Kirk, John W. Nickolls, 2017 (NVIDIA Corporation) - Provides specifications and architectural details of the Volta GPU, including the introduction of Tensor Cores.