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
MLOps Principles at Scale
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- Hidden Technical Debt in Machine Learning Systems, D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison, 2015 Advances in Neural Information Processing Systems (NeurIPS) 28 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) - Identifies core challenges leading to complexity and maintenance burden in production ML systems.
- Engineering MLOps: Machine Learning Operations at Scale, Emmanuel Raj, Harish Lakshmanan, Anurag Agarwal, 2022 (O'Reilly Media) - A comprehensive guide to designing and implementing scalable MLOps platforms, covering abstraction, workflows, and infrastructure.
- Effective MLOps: Enabling Reproducibility and Auditability for Machine Learning Experiments, Klaus Schelter, Simon Klinger, Michael Fechner, Tobias Schmidt, Andreas Schmidt, 2020 Proceedings of the 3rd Workshop on MLOps Systems (MLOps'20) (ACM) DOI: 10.1145/3429381.3432924 - Discusses strategies and challenges for achieving reproducibility and auditability of ML experiments and models.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready AI, Chip Huyen, 2022 (O'Reilly Media) - This book provides practical guidance on designing ML systems, including chapters on monitoring, testing, and cost optimization for production workloads.