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Duration 14 hours
Course Outline
Introduction to AI Deployment
- Overview of the AI deployment lifecycle
- Challenges associated with deploying AI agents to production
- Key considerations: scalability, reliability, and maintainability
Containerization and Orchestration
- Foundations of Docker and containerization
- Utilizing Kubernetes for orchestrating AI agents
- Best practices for managing containerized AI applications
Serving AI Models
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch versus real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Managing rolling updates and version control
Monitoring and Optimization
- Implementing tools to monitor AI agent performance
- Analyzing model drift and identifying retraining needs
- Optimizing resource utilization and scalability
Security and Governance
- Ensuring compliance with data privacy regulations
- Securing 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
- Setting up monitoring for AI performance and resource usage
Summary and Next Steps
Requirements
- Proficiency in Python programming
- A solid understanding of machine learning workflows
- Familiarity with containerization tools, particularly Docker
- Experience with DevOps practices (recommended)
Target Audience
- MLOps engineers
- DevOps professionals
