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Course Outline
Establishing the Fundamentals of Agentic Systems in Production
- Agentic architectures: encompassing loops, tools, memory, and orchestration layers
- The agent lifecycle: spanning development, deployment, and ongoing operation
- Addressing the complexities of managing agents at production scale
Infrastructure Strategies and Deployment Models
- Implementing agents within containerized and cloud-based environments
- Scaling methodologies: balancing horizontal versus vertical scaling, concurrency, and throttling
- Orchestrating multi-agent systems and managing workload distribution
Monitoring Frameworks and Observability
- Core metrics: tracking latency, success rates, memory consumption, and agent call depth
- Tracing agent activities and visualizing call graphs
- Deploying observability stacks using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Regulatory Compliance
- Establishing centralized logging and structured event aggregation
- Ensuring compliance and auditability within agentic workflows
- Creating audit trails and replay mechanisms to facilitate debugging
Performance Optimization and Resource Efficiency
- Minimizing inference overhead and refining agent orchestration cycles
- Utilizing model caching and lightweight embeddings to accelerate retrieval
- Conducting load testing and stress analysis for AI pipelines
Cost Management and Governance
- Analyzing cost drivers for agents: including API calls, memory, compute resources, and external integrations
- Monitoring agent-specific expenses and establishing chargeback models
- Enforcing automation policies to curb agent sprawl and eliminate idle resource usage
CI/CD Integration and Rollout Strategies for Agents
- Embedding agent pipelines into CI/CD infrastructure
- Applying testing, versioning, and rollback tactics for iterative agent improvements
- Implementing progressive rollouts and secure deployment protocols
Failure Recovery and Reliability Engineering
- Engineering for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns to ensure agent reliability
- Developing incident response and post-mortem frameworks for AI operations
Capstone Project
- Constructing and deploying an agentic AI system equipped with comprehensive monitoring and cost tracking
- Simulating load conditions, assessing performance, and refining resource utilization
- Presenting the final architecture and monitoring dashboard to peers
Conclusions and Future Directions
Requirements
- Robust comprehension of MLOps and production machine learning systems
- Hands-on experience with containerized deployments (Docker/Kubernetes)
- Proficiency with cloud cost optimization strategies and observability tools
Target Audience
- MLOps engineers
- Site Reliability Engineers (SREs)
- Engineering managers responsible for AI infrastructure
21 Hours
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
