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Course Outline
Foundations of AI Deployment
- Overview of the AI deployment lifecycle
- Key challenges in deploying AI agents to production
- Critical factors: scalability, reliability, and maintainability
Containerization and Orchestration
- Basics of Docker and containerization concepts
- Orchestrating AI agents using Kubernetes
- 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 prediction workloads
CI/CD for AI Agents
- Establishing CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Implementing rolling updates and managing version control
Monitoring and Optimization
- Deploying monitoring tools for AI agent performance
- Evaluating model drift and determining retraining necessities
- Optimizing resource utilization and scaling capabilities
Security and Governance
- Ensuring compliance with data privacy regulations
- Securing AI deployment pipelines and APIs
- Implementing auditing and logging 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 Pathways
Requirements
- Strong proficiency in Python programming
- Comprehensive understanding of machine learning workflows
- Working knowledge of containerization technologies, such as Docker
- Background in DevOps practices (advisable)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
