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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

Chapter 3: Chapter 3: Advanced Resource Orchestration with Kubernetes

While the previous chapter focused on the mechanics of a single distributed training job, production systems must manage many such jobs concurrently and efficiently. Operating directly on cloud virtual machines presents challenges in scheduling, fault tolerance, and resource utilization. Container orchestration provides the necessary layer of abstraction to solve these problems.

This chapter details the use of Kubernetes for orchestrating large-scale ML workloads. You will configure production-grade features for managing the entire lifecycle of models on a shared compute cluster. We begin by using KubeFlow to define and automate ML pipelines. Next, you will learn to manage specialized hardware through advanced GPU scheduling, including time-slicing and Multi-Instance GPU (MIG) configurations. We will then address operational efficiency by setting up cluster autoscaling to match compute supply with workload demand and by building strategies for using low-cost spot instances. The chapter concludes with methods for implementing multi-tenancy, enabling multiple teams to share infrastructure securely and with defined resource boundaries.

Sections

3.1 Managing ML Workflows with KubeFlow Pipelines
3.2 Advanced GPU Scheduling and Sharing
3.3 Cluster Autoscaling for Dynamic ML Workloads
3.4 Strategies for Using Spot and Preemptible Instances
3.5 Multi-Tenancy with Namespaces, Quotas, and Priority Classes
3.6 Practice: Configure a GPU-Aware Autoscaling Group

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