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Course Outline
Introduction to AI Deployment
- Overview of the AI deployment lifecycle
- Challenges associated with moving AI agents to production
- Core considerations: 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
- Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing 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 testing and validation for AI models
- Executing rolling updates and managing version control
Monitoring and Optimization
- Deploying monitoring tools to track AI agent performance
- Identifying model drift and retraining requirements
- Enhancing resource usage and scalability
Security and Governance
- Complying with data privacy regulations
- Securing AI deployment pipelines and API endpoints
- Implementing auditing and logging for AI systems
Hands-On Activities
- Containerizing an AI agent with 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
- Comprehensive understanding of machine learning workflows
- Knowledge of containerization tools such as Docker
- Experience with DevOps practices (suggested)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
