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
Model Retraining and Update Strategies
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready ML Applications, Chip Huyen, 2022 (O'Reilly Media) - This book offers a comprehensive guide to building, deploying, and maintaining machine learning systems, including MLOps practices, monitoring, drift detection, and deployment strategies relevant to the section.
- MLOps: Continuous delivery and automation pipelines in machine learning, Lak Lakshmanan, Sara Robinson, Michael Munn, 2020 (Google Cloud) - This official guide provides practical advice and best practices for implementing MLOps, covering CI/CD pipelines, model monitoring, deployment patterns, and model governance from a cloud perspective.
- A Survey on Concept Drift Adaption, João Gama, Indrė Žliobaitė, Albert Bifet, Myra Spiliopoulou, Paul Van Roy, 2014 ACM Computing Surveys, Vol. 46 (Association for Computing Machinery) DOI: 10.1145/2523813 - This foundational survey paper provides an overview of various types of concept drift and methods for adapting machine learning models to changing data distributions, directly addressing a key trigger for model updates.
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