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Course Outline
Introduction to AI Deployment
- An overview of the AI deployment lifecycle
- Key challenges in moving AI agents to production
- Critical factors: scalability, reliability, and maintainability
Containerization and Orchestration
- Fundamentals of Docker and containerization
- Orchestrating AI agents using Kubernetes
- Best practices for managing containerized AI applications
Serving AI Models
- Exploring model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch processing versus real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Handling rolling updates and version control
Monitoring and Optimization
- Integrating monitoring tools to track AI agent performance
- Identifying model drift and assessing retraining needs
- Optimizing resource usage and scalability
Security and Governance
- Compliance with data privacy regulations
- Securing AI deployment pipelines and associated APIs
- Implementing auditing and logging mechanisms for AI applications
Hands-On Activities
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Setting up performance and resource usage monitoring
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- A solid understanding of machine learning workflows
- Working knowledge of containerization tools such as Docker
- Experience with DevOps practices (highly recommended)
Target Audience
- MLOps Engineers
- DevOps Professionals
14 Hours
