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Course Outline
Foundations of AI Deployment
- An overview of the AI deployment lifecycle
- Addressing the complexities of deploying AI agents into production
- Essential factors: scalability, reliability, and maintainability
Containerisation and Orchestration
- Core concepts of Docker and the fundamentals of containerisation
- Applying Kubernetes for the orchestration of AI agents
- Best practices for managing containerised AI applications
Serving AI Models
- An introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing both batch and real-time prediction workflows
CI/CD for AI Agents
- Establishing CI/CD pipelines dedicated to AI deployments
- Automating the testing and validation processes for AI models
- Implementing rolling updates and effective version control
Monitoring and Optimisation
- Deploying monitoring tools to track AI agent performance
- Identifying model drift and determining retraining requirements
- Enhancing resource utilisation and system scalability
Security and Governance
- Complying with data privacy regulations and standards
- Safeguarding AI deployment pipelines and associated APIs
- Conducting audits and maintaining logs for AI applications
Practical Exercises
- Containerising an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Recap and Future Directions
Requirements
- Strong proficiency in Python programming
- A solid grasp of machine learning workflows
- Working knowledge of containerisation tools such as Docker
- Background experience with DevOps practices (advisable)
Target Audience
- MLOps engineers
- DevOps professionals
14 Hours
