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Duration 14 hours
Course Outline
Foundations of AI Deployment
- Comprehensive view of the AI deployment lifecycle
- Key challenges encountered when moving AI agents to production
- Critical factors: scalability, reliability, and ease of maintenance
Containerization and Orchestration Strategies
- Core concepts of Docker and containerization
- Applying Kubernetes for the orchestration of AI agents
- Best practices for managing container-based AI applications
Serving AI Models Efficiently
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating REST APIs for AI agent inference tasks
- Managing both batch processing and real-time prediction workloads
CI/CD Integration for AI Agents
- Configuring CI/CD pipelines specifically for AI deployments
- Automating the testing and validation phases for AI models
- Implementing rolling updates and managing version control
Monitoring and Performance Optimization
- Deploying monitoring tools to track AI agent performance
- Evaluating model drift and identifying retraining requirements
- Enhancing resource utilization and system scalability
Security and Governance Frameworks
- Ensuring alignment with data privacy regulations
- Hardening AI deployment pipelines and API security
- Implementing audit trails and logging for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Establishing monitoring systems for AI performance and resource consumption
Concluding Summary and Future Directions
Requirements
- Strong proficiency in Python programming
- Foundational knowledge of machine learning workflows
- Basic familiarity with containerization solutions such as Docker
- Practical experience with DevOps methodologies (suggested)
Intended Audience
- MLOps engineers
- DevOps specialists
