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Duration 14 hours
Course Outline
Foundations of AI Deployment
- Understanding the complete AI deployment lifecycle
- Navigating challenges associated with releasing AI agents to production
- Core focus areas: scalability, reliability, and maintainability
Containerization and Orchestration Strategies
- Basics of Docker and the fundamentals of containerization
- Utilizing Kubernetes for orchestrating AI agents
- Best practices for maintaining containerized AI applications
AI Model Serving Mechanisms
- Survey of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Constructing REST APIs for AI agent inference tasks
- Managing batch processing versus real-time prediction streams
CI/CD Integration for AI Agents
- Configuring CI/CD pipelines tailored for AI deployments
- Automating the testing and validation processes for AI models
- Implementing rolling updates and version control management
Performance Monitoring and Optimization
- Deploying monitoring tools to track AI agent performance
- Evaluating model drift and identifying retraining requirements
- Enhancing resource efficiency and system scalability
Security Frameworks and Governance
- Meeting data privacy regulatory compliance standards
- Protecting AI deployment pipelines and API endpoints
- Implementing auditing and logging protocols for AI applications
Practical Laboratory Sessions
- Containerizing an AI agent using Docker
- Deploying an AI agent within a Kubernetes cluster
- Establishing monitoring for AI performance and resource consumption
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Comprehensive understanding of machine learning workflows
- Working knowledge of containerization tools such as Docker
- Practical experience with DevOps practices (suggested)
Target Audience
- MLOps engineers
- DevOps professionals
