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Duration 21 hours
Course Outline
Comprehending Mastra Architecture and Operational Concepts
- Essential components and their functions in production.
- Integration patterns supported for enterprise environments.
- Key considerations for security and governance.
Readying Environments for Agent Deployment
- Setting up container runtime environments.
- Configuring Kubernetes clusters to handle AI agent workloads.
- Managing secrets, credentials, and configuration stores.
Deploying Mastra AI Agents
- Packaging agents for deployment.
- Leveraging GitOps and CI/CD for automated delivery.
- Validating deployments through structured testing.
Scaling Strategies for Production AI Agents
- Horizontal scaling patterns.
- Autoscaling using HPA, KEDA, and event-driven triggers.
- Strategies for load distribution and request handling.
Observability, Monitoring, and Logging for AI Agents
- Best practices for telemetry instrumentation.
- Integration with Prometheus, Grafana, and logging stacks.
- Monitoring agent performance, drift, and operational anomalies.
Optimizing Performance and Resource Efficiency
- Profiling agent workloads.
- Enhancing inference performance and reducing latency.
- Cost-optimization strategies for large-scale agent deployments.
Reliability, Resilience, and Failure Handling
- Designing for resiliency under load.
- Implementing circuit-breaking, retries, and rate limiting.
- Disaster recovery planning for agent-based systems.
Integrating Mastra into Enterprise Ecosystems
- Interfacing with APIs, data pipelines, and event buses.
- Aligning agent deployments with enterprise DevSecOps practices.
- Adapting architectures to existing platform environments.
Summary and Next Steps
Requirements
- Working knowledge of containerization and orchestration.
- Experience with CI/CD workflows.
- Familiarity with AI model deployment concepts.
Audience
- DevOps engineers.
- Backend developers.
- Platform engineers managing AI workloads.
