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Duration 21 hours
Course Outline
Grasping Mastra Architecture and Operational Principles
- Essential components and their function in production
- Integration patterns suitable for enterprise contexts
- Security and governance frameworks
Setting Up Environments for Agent Deployment
- Configuring container runtime setups
- Preparing Kubernetes clusters for AI agent tasks
- Handling secrets, credentials, and configuration storage
Deploying Mastra AI Agents
- Packaging agents for release
- Leveraging GitOps and CI/CD for automated delivery
- Verifying deployments via structured testing
Scaling Tactics for Production AI Agents
- Horizontal scaling models
- Autoscaling using HPA, KEDA, and event-based triggers
- Load balancing and request processing methods
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 irregularities
Enhancing Performance and Resource Efficiency
- Profiling agent workloads
- Boosting inference speed and cutting down latency
- Cost-optimization strategies for large-scale agent deployments
Reliability, Resilience, and Failure Management
- Designing for stability under high load
- Applying circuit breakers, retries, and rate limiting
- Disaster recovery strategies for agent-based systems
Integrating Mastra into Enterprise Ecosystems
- Connecting with APIs, data pipelines, and event buses
- Aligning agent deployments with enterprise DevSecOps standards
- Adapting architectures to fit existing platform environments
Wrap-up and Future Steps
Requirements
- A solid grasp of containerization and orchestration principles
- Practical experience with CI/CD pipelines
- Knowledge of AI model deployment fundamentals
Target Audience
- DevOps engineers
- Backend developers
- Platform engineers managing AI workloads
