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Course Outline
Foundations of AI Deployment
- Overview of the AI deployment lifecycle
- Common challenges when moving AI agents to production
- Essential factors: scalability, reliability, and maintainability
Containerization and Orchestration
- Basics of Docker and containerization concepts
- Utilizing Kubernetes for orchestrating AI agents
- Best practices for managing containerized AI applications
AI Model Serving
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating REST APIs for AI agent inference
- Managing batch versus real-time prediction workloads
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployment
- Automating the testing and validation of AI models
- Managing rolling updates and version control
Monitoring and Optimization
- Deploying monitoring tools for AI agent performance
- Assessing model drift and determining retraining needs
- Optimizing resource usage and system scalability
Security and Governance
- Ensuring adherence to data privacy regulations
- Hardening AI deployment pipelines and APIs
- Implementing auditing and logging for AI applications
Practical Workshops
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Establishing monitoring for AI performance and resource consumption
Summary and Future Directions
Requirements
- Strong command of Python programming
- Conceptual grasp of machine learning workflows
- Working knowledge of containerization technologies such as Docker
- Practical experience with DevOps methodologies (suggested)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
