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Advanced AI Infrastructure Design and Optimization

章节 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

章节 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

章节 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

章节 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

章节 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

章节 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

Advanced AI Infrastructure Design and Optimization

先决条件: Python, ML, and Cloud basics

级别:

高级

您将学到什么

Distributed Training Systems
Design and implement distributed training jobs using data, model, and pipeline parallelism with frameworks like DeepSpeed and PyTorch FSDP.
Advanced Kubernetes for ML
Orchestrate complex ML workloads on Kubernetes with advanced scheduling for GPUs, spot instances, and multi-tenancy.
Inference Optimization
Architect and deploy high-throughput, low-latency inference services using model compilation, quantization, and specialized serving frameworks.
Scalable Data Systems
Construct scalable feature stores and data processing pipelines for both real-time and batch computation.
AI FinOps
Implement cost management, attribution, and optimization strategies specifically for AI and ML cloud expenditures.
Production MLOps Pipelines
Build automated, end-to-end MLOps pipelines incorporating CI/CD, data versioning, and model monitoring.

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