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Duration 21 hours
Course Outline
Exploring Mastra Architecture and Operational Principles
- Essential components and their functions in production
- Integration patterns suitable for enterprise contexts
- Key security and governance factors
Setting Up Environments for Agent Deployment
- Configuring container runtimes
- Readying Kubernetes clusters for AI agent tasks
- Handling secrets, credentials, and configuration storage
Deploying Mastra AI Agents
- Preparing agents for release
- Leveraging GitOps and CI/CD for streamlined delivery
- Verifying deployments via systematic testing
Scaling Tactics for Production AI Agents
- Horizontal scaling approaches
- Automated scaling using HPA, KEDA, and event-driven mechanisms
- Techniques for load balancing and request management
Observability, Monitoring, and Logging for AI Agents
- Best practices for telemetry instrumentation
- Incorporating Prometheus, Grafana, and logging frameworks
- MTracking agent performance, drift, and operational irregularities
Enhancing Performance and Resource Efficiency
- Analyzing agent workload profiles
- Boosting inference speed and lowering latency
- Strategies for cost efficiency in large-scale agent deployments
Ensuring Reliability, Resilience, and Failure Management
- Designing systems for stability under high load
- Applying circuit-breaking, retry logic, and rate limiting
- Planning disaster recovery for agent-based architectures
Embedding Mastra in Enterprise Ecosystems
- Connecting with APIs, data streams, and event buses
- Aligning agent operations with enterprise DevSecOps standards
- Adapting architectures to fit current platform landscapes
Overview and Future Actions
Requirements
- Competence in containerization and orchestration practices
- Hands-on experience with CI/CD pipelines
- Knowledge of AI model deployment methodologies
Target Audience
- DevOps engineers
- Backend developers
- Platform engineers overseeing AI workloads
