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Course Outline
Introduction to AI Deployment
- An overview of the AI deployment lifecycle
- Common challenges when moving AI agents to production
- Key factors: scalability, reliability, and maintainability
Containerisation and Orchestration
- Foundations of Docker and containerisation
- Orchestrating AI agents with Kubernetes
- Best practices for managing containerised AI applications
Serving AI Models
- Exploring model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating 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 Optimisation
- Deploying monitoring tools to track AI agent performance
- Assessing model drift and identifying retraining needs
- Optimising resource usage and scalability
Security and Governance
- Ensuring compliance with data privacy regulations
- Securing AI deployment pipelines and APIs
- Implementing auditing and logging for AI applications
Practical Exercises
- Containerising an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Setting up monitoring for AI performance and resource consumption
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- A solid understanding of machine learning workflows
- Knowledge of containerisation tools, such as Docker
- Experience with DevOps practices (highly recommended)
Target Audience
- MLOps engineers
- DevOps professionals
14 Hours
