Agent Optimization & Deployment
This program covers techniques for optimizing agent performance, cost reduction, ensuring reliability, and strategies for deploying agents in production environments (e.g., cloud platforms, containerization). Includes monitoring and scaling.
Pre-Requisite
- Advanced Agent Architectures & Multi-Agent Systems
- Practical experience with cloud platforms (AWS, Azure, GCP) & DevOps principles.
Course Outline
- 4 Sections
- 20 Lessons
- 4 Days
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- DAY 1 - Agent Performance & Cost Optimization5
- 1.1Identifying Performance Bottlenecks in Agents
- 1.2Token Optimization Strategies (Prompt compression, response parsing)
- 1.3Caching Mechanisms for LLM calls and tool results
- 1.4Cost Monitoring and Reduction Techniques for AI Services
- 1.5Hands-on: Analyzing agent performance and implementing basic optimization strategies.
- DAY 2 - Deployment Strategies for AI Agents5
- 2.1Containerization with Docker for Agent Packaging
- 2.2Orchestration with Kubernetes for Scaling and Management
- 2.3Serverless Deployment Options (e.g., AWS Lambda, Azure Functions, Google Cloud Functions)
- 2.4Choosing the Right Deployment Model
- 2.5Hands-on: Containerizing an agent and deploying it to a local Docker environment.
- DAY 3 - Monitoring, Logging & Reliability5
- 3.1Implementing Robust Logging for Agent Activity and Failures
- 3.2Monitoring Agent Performance and Health (Metrics, Dashboards)
- 3.3Error Handling and Alerting in Production Environments
- 3.4Strategies for Ensuring Agent Reliability and Uptime
- 3.5Hands-on: Setting up basic monitoring and logging for a deployed agent.
- DAY 4 - Scaling & A/B Testing Agents5
- 4.1Strategies for Scaling Agent Deployments (Horizontal vs. Vertical Scaling)
- 4.2Load Balancing and Traffic Management for Agents
- 4.3A/B Testing and Experimentation for Agent Improvements
- 4.4Version Control and Rollback Strategies for Agent Deployments
- 4.5Hands-on: Exploring scaling configurations and discussing A/B testing frameworks for agents.
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