Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AI Deployment
- Insights into the AI deployment lifecycle.
- Challenges associated with launching AI agents in production.
- Core factors: scalability, reliability, and maintainability.
Containerization and Orchestration
- Fundamentals of Docker and the containerization process.
- Leveraging Kubernetes for AI agent orchestration.
- Optimal 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 predictions.
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployments.
- Automation of AI model testing and validation processes.
- Managing rolling updates and version control.
Monitoring and Optimization
- Deploying monitoring solutions for AI agent performance.
- Evaluating model drift and identifying retraining requirements.
- Enhancing resource efficiency and scalability.
Security and Governance
- Compliance with data privacy regulations.
- Hardening AI deployment pipelines and APIs.
- Implementing auditing and logging for AI applications.
Practical Activities
- Encapsulating an AI agent using Docker.
- Releasing an AI agent via Kubernetes.
- Configuring monitoring for AI performance and resource consumption.
Conclusions and Future Directions
Requirements
- Strong proficiency in Python programming.
- A solid grasp of machine learning workflows.
- Knowledge of containerization platforms, specifically Docker.
- Practical experience with DevOps methodologies (advisable).
Target Audience
- MLOps Engineers.
- DevOps Specialists.
14 Hours
