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Duration 14 hours (2 days)
Course Outline
Introduction to AI Deployment
- Comprehensive view of the AI deployment lifecycle
- Primary obstacles in deploying AI agents to production
- Critical factors: scalability, reliability, and maintainability
Containerization and Orchestration
- Fundamentals of Docker and containerization
- Utilizing Kubernetes for AI agent orchestration
- Best practices for managing containerized AI applications
Serving AI Models
- Review of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch versus real-time prediction workflows
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployment
- Streamlining the testing and validation of AI models
- Implementing rolling updates and version control management
Monitoring and Optimization
- Integrating monitoring tools for AI agent performance
- Evaluating model drift and determining retraining necessity
- Enhancing resource utilization and scalability
Security and Governance
- Adhering to data privacy regulations
- Protecting AI deployment pipelines and APIs
- Implementing auditing and logging for AI applications
Hands-On Activities
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Solid grasp of machine learning workflows
- Working knowledge of containerization technologies such as Docker
- Practical experience with DevOps methodologies (advisable)
Intended Audience
- MLOps engineers
- DevOps specialists
