Deploying Diffusion Models at Scale
Chapter 1: Scaling Challenges and Architectures
Computational Requirements of Diffusion Models
Latency and Throughput Considerations
Architectural Patterns for Generative AI Deployment
Synchronous vs. Asynchronous Processing
MLOps Principles for Diffusion Models
Chapter 2: Optimizing Diffusion Models for Inference
Inference Bottlenecks in Diffusion Processes
Model Quantization Techniques (INT8, FP16)
Knowledge Distillation for Diffusion Models
Sampler Optimization Strategies
Hardware Acceleration (GPUs, TPUs)
Compiler Optimization (TensorRT, OpenVINO)
Benchmarking Inference Performance
Hands-on Practical: Optimizing a Diffusion Model
Chapter 3: Infrastructure for Scalable Deployment
Containerizing Diffusion Models with Docker
GPU Resource Management in Containers
Orchestration with Kubernetes
Managing GPU Nodes in Kubernetes
Autoscaling Strategies for Inference Workloads
Serverless GPU Inference Options
Storage Considerations for Models and Data
Hands-on Practical: Deploying on Kubernetes
Chapter 4: Building Scalable Inference APIs
API Design Patterns for Generative Models
Handling Long-Running Generation Tasks
Request Batching Techniques
Implementing Request Queues
Rate Limiting and Throttling
Authentication and Authorization
API Versioning Strategies
Hands-on Practical: Building an Inference API
Chapter 5: Monitoring and Maintaining Deployed Models
Essential Metrics for Diffusion Model Deployment
Setting up Logging and Tracing
Monitoring Tools and Platforms
Detecting Performance Regressions
Monitoring Generation Quality
Cost Monitoring and Alerting
Model Retraining and Update Strategies
Hands-on Practical: Setting up Monitoring
Chapter 6: Advanced Deployment Techniques
Multi-Region and Global Deployment Strategies
Canary Releases and A/B Testing Models
Advanced Cost Optimization Strategies
Handling GPU Failures and Spot Instance Interruptions
Optimizing Data Transfer Costs
Cold Starts in Serverless and Container Environments
Load Balancing Strategies for Stateful/Long Tasks
Monitoring Tools and Platforms
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- Prometheus Documentation, The Prometheus Authors, 2024 - Official documentation describing the Prometheus monitoring system, its architecture, data model, querying language PromQL, and how to use various exporters.
- Grafana Documentation, Grafana Labs, 2024 - Official documentation for Grafana, detailing its capabilities for creating interactive dashboards, visualizing time-series data, and connecting to diverse data sources.
- OpenTelemetry Documentation, The OpenTelemetry Authors, 2025 - Official documentation for OpenTelemetry, outlining its standardized approach for generating, collecting, and exporting metrics, logs, and traces.
- Dapper, a Large-Scale Distributed Systems Tracing Infrastructure, Benjamin H. Sigelman, Luiz André Barroso, Mike Burrows, Pat Stephenson, Manoj Plakal, Donald Beaver, Saul Jaspan, Chandan Shanbhag, 2010 USENIX Large Installation System Administration Conference (LISA '10) (IEEE) DOI: 10.1109/LISA.2010.59 - This paper introduced Google's Dapper system, a tracing infrastructure that set the standard for modern distributed tracing systems.
- Grafana Loki Documentation, Grafana Labs, 2024 (Grafana Labs) - Official documentation for Grafana Loki, an aggregation system for logs that uses a Prometheus-like approach for indexing and querying, designed for operational efficiency.