Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Understanding Mastra Architecture and Operational Concepts
- Core components and their production roles
- Supported integration patterns for enterprise environments
- Security and governance considerations
Preparing Environments for Agent Deployment
- Configuring container runtime environments
- Preparing Kubernetes clusters for AI agent workloads
- Managing secrets, credentials, and config stores
Deploying Mastra AI Agents
- Packaging agents for deployment
- Using GitOps and CI/CD for automated delivery
- Validating deployments through structured testing
Scaling Strategies for Production AI Agents
- Horizontal scaling patterns
- Autoscaling with HPA, KEDA, and event-driven triggers
- Load distribution and request-handling strategies
Observability, Monitoring, and Logging for AI Agents
- Telemetry instrumentation best practices
- Integrating Prometheus, Grafana, and logging stacks
- Tracking agent performance, drift, and operational anomalies
Optimizing Performance and Resource Efficiency
- Profiling agent workloads
- Improving inference performance and reducing latency
- Cost-optimization approaches 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
- Adapting architectures to existing platform environments
Summary and Next Steps
Requirements
- An understanding of containerization and orchestration
- Experience with CI/CD workflows
- Familiarity with AI model deployment concepts
Audience
- DevOps engineers
- Backend developers
- Platform engineers responsible for AI workloads
21 Hours
