Deploying Diffusion Models at Scale
Prerequisites: ML/DL, Python, Cloud/Ops
Level:
What You'll Learn
Model Optimization
Apply techniques like quantization and distillation to optimize diffusion models for faster and cheaper inference.
Scalable Infrastructure
Design and implement scalable infrastructure using cloud services, containers, and orchestration for diffusion model deployment.
Inference API Development
Build robust and efficient APIs for serving diffusion model inference requests at scale.
Performance Tuning
Analyze and tune the performance of deployed diffusion models, addressing latency and throughput bottlenecks.
MLOps for Generative Models
Implement monitoring, logging, and maintenance strategies tailored for diffusion models in production.
Cost Management
Develop strategies for managing and optimizing the operational costs associated with large-scale diffusion model deployment.