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Duration 21 hours
Course Outline
Grasping Mastra Architecture and Operational Principles
- Key components and their roles in production systems
- Integration patterns suited for enterprise environments
- Security and governance frameworks
Setting Up Environments for Agent Deployment
- Configuring container runtimes
- Provisioning Kubernetes clusters for AI agent workloads
- Managing secrets, credentials, and configuration stores
Deploying Mastra AI Agents
- Packaging agents for release
- Leveraging GitOps and CI/CD for automated delivery
- Verifying deployments via structured testing
Scaling Production AI Agents
- Horizontal scaling approaches
- 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, model drift, and operational anomalies
Optimizing Performance and Resource Efficiency
- Profiling agent workloads
- Enhancing inference speed and reducing latency
- Cost-efficiency strategies for large-scale agent deployments
Reliability, Resilience, and Failure Management
- Designing for resiliency under high load
- Implementing circuit breakers, retries, and rate limiting
- Disaster recovery planning for agent-based systems
Embedding Mastra into Enterprise Ecosystems
- Interfacing with APIs, data pipelines, and event buses
- Aligning agent deployments with enterprise DevSecOps practices
- Adapting architectures to fit existing platform environments
Summary and Next Steps
Requirements
- Working knowledge of containerization and orchestration tools.
- Practical experience with CI/CD pipelines.
- Basic understanding of AI model deployment principles.
Target Audience
- DevOps Engineers
- Backend Developers
- Platform Engineers managing AI workloads
