Master advanced orchestration patterns for coordinating multiple AI models. Learn to build scalable, production-ready multi-model systems that handle complex workflows, model selection, and intelligent routing.
Explore foundational patterns for orchestrating multiple AI models. Learn about model selection strategies, routing algorithms, and architectural approaches for building scalable multi-model systems.
Master techniques for dynamically selecting models based on task requirements. Learn cost-aware routing, performance optimization, and decision trees for model selection in production systems.
Learn to build complex workflows combining multiple models and processing steps. Discover techniques for state management, error handling, and resilient pipeline design across distributed systems.
Master production observability for multi-model systems. Learn performance monitoring, cost tracking per model, optimization strategies, and real-time debugging for complex orchestrated workflows.
Study real-world case studies of multi-model orchestration in production. Learn deployment patterns, scaling strategies, and lessons from industry implementations managing complex AI workloads.
By the end of this module, you will:
Create a production-ready orchestration system that routes requests to multiple models based on task complexity, cost, and performance requirements. Implement monitoring, auto-scaling, and sophisticated fallback mechanisms.
What you'll build:
Duration: 5-6 hours | Stack: Python, FastAPI, Celery, Docker, Kubernetes
Start Lab →Test your understanding with 20 advanced questions covering multi-model orchestration, intelligent routing, workflow management, and production deployment strategies. You need 70% or higher to pass.