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Duration 14 hours
Course Outline
Fundamentals of AI Deployment
- Insight into the AI deployment lifecycle
- Obstacles encountered when moving AI agents to production
- Critical factors: scalability, reliability, and long-term maintainability
Containerization and Orchestration Strategies
- Basics of Docker and containerization
- Employing Kubernetes to orchestrate AI agents
- Best practices for overseeing containerized AI applications
Serving AI Models
- Review of model serving frameworks (such as TensorFlow Serving, TorchServe)
- Creating REST APIs for AI agent inference
- Managing the distinction between batch and real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines specific to AI deployments
- Automating the testing and validation of AI models
- Executing rolling updates and managing version control
Monitoring and Optimization
- Implementing monitoring solutions for AI agent performance
- Assessing model drift and identifying retraining requirements
- Refining resource usage and scalability
Security and Governance
- Complying with data privacy regulations
- Protecting AI deployment pipelines and APIs
- Conducting audits and maintaining logs for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Conclusion and Future Directions
Requirements
- Strong command of Python programming
- A solid grasp of machine learning workflows
- Working knowledge of containerization platforms like Docker
- Background in DevOps practices (advisable)
Target Audience
- MLOps engineers
- DevOps specialists
