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Course Outline
Grasping Mastra Architecture and Operational Principles
- Key components and their function in production
- Integration patterns supported for enterprise settings
- Security and governance factors to consider
Setting Up Environments for Agent Deployment
- Configuring container runtime environments
- Preparing Kubernetes clusters for AI agent workloads
- Managing secrets, credentials, and configuration stores
Deploying Mastra AI Agents
- Packaging agents for release
- Utilizing GitOps and CI/CD for automated delivery
- Verifying deployments via structured testing
Scaling Strategies for Production AI Agents
- Horizontal scaling methods
- Autoscaling via HPA, KEDA, and event-driven triggers
- Load balancing and request handling tactics
Observability, Monitoring, and Logging for AI Agents
- Best practices for telemetry instrumentation
- Integrating Prometheus, Grafana, and logging stacks
- Monitoring agent performance, drift, and operational anomalies
Enhancing Performance and Resource Efficiency
- Profiling agent workloads
- Boosting inference performance and lowering latency
- Cost-efficiency strategies for large-scale agent deployments
Reliability, Resilience, and Failure Management
- Designing for resiliency under high load
- Applying circuit-breaking, retries, and rate limiting
- Disaster recovery planning for agent-based systems
Integrating Mastra into Enterprise Ecosystems
- Connecting with APIs, data pipelines, and event buses
- Aligning agent deployments with enterprise DevSecOps
- Adapting architectures to current platform environments
Overview and Subsequent Steps
Requirements
- A solid grasp of containerization and orchestration
- Hands-on experience with CI/CD workflows
- Knowledge of AI model deployment principles
Target Audience
- DevOps engineers
- Backend developers
- Platform engineers managing AI workloads
21 Hours