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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 1: Chapter 1: Architectural Patterns for AI Platforms

Building a machine learning model is one part of the process. Making it perform reliably at scale is a different and more complex engineering challenge. The effectiveness of any advanced AI system is fundamentally constrained by the design of its underlying infrastructure. Seemingly small architectural choices in compute, networking, and storage can lead to significant differences in training time, inference latency, and operational cost.

This chapter establishes the foundational hardware and software patterns for high-performance AI platforms. We will cover the specific infrastructure components required to support demanding machine learning workloads.

You will learn to:

Apply MLOps principles to large, production-oriented environments.
Analyze the trade-offs between CPU, GPU, and TPU architectures for specific ML tasks.
Understand the role of high-bandwidth interconnects like NVLink and InfiniBand in multi-node training.
Compare different storage solutions, from object storage to parallel file systems, for ML data access patterns.
Evaluate network topologies to prevent communication bottlenecks in distributed clusters.

We will conclude with a hands-on practical to configure your local and cloud environments with the necessary tooling for the course. This setup will prepare you for the technical implementations in the chapters that follow.

Sections

1.1 MLOps Principles at Scale
1.2 Compute Selection: CPU, GPU, and TPU Architectures
1.3 High-Bandwidth Interconnects for Distributed Systems
1.4 Storage Solutions for Large-Scale AI Datasets
1.5 Networking Topologies for ML Clusters
1.6 Hands-on Practical: Environment and Tooling Setup

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