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
High-Throughput Data Processing with Spark and Ray
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- Apache Spark Documentation, The Apache Software Foundation, 2024 - Provides comprehensive guidance on Spark's architecture, DataFrame API, PySpark, and distributed processing capabilities.
- Ray Documentation, Anyscale, 2024 - Offers detailed information on Ray Core, Ray Data, task and actor primitives, and building distributed Python applications.
- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Blake Goldin, Joseph Hellerstein, Mohammad Isard, S. Krishnan, Haoyuan Li, Scott McVeety, Andy Konwinski, Patrick Wendell, Adam Wilde, Michael Franklin, Ion Stoica, 2012 Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI '12) (USENIX) DOI: 10.1145/2228298.2228301 - Introduces the RDD abstraction, which is fundamental to Spark's design for fault-tolerant and efficient distributed data processing.
- Ray: A Distributed System for AI Applications, Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Gungor, Eric Choi, Joseph E. Gonzalez, Ion Stoica, 2018 Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI '18) - Describes Ray's flexible architecture for general-purpose distributed Python computation, designed to support diverse AI workloads.