
Advanced AI and ML Infrastructure
This course focuses on designing, deploying, and managing scalable, efficient, and secure AI/ML systems. It covers advanced concepts such as distributed training, MLOps, CI/CD pipelines, hybrid and edge AI, AI-specific security, and sustainable infrastructure. Participants will work with cutting-edge tools and techniques to build robust, production-ready AI/ML systems, preparing them for real-world challenges in large-scale deployments.
Add a Title
Add paragraph text. Click “Edit Text” to update the font, size and more. To change and reuse text themes, go to Site Styles.
Course Duration:
27 hours
Level:
Advanced

Course Objectives
Understand the architecture and components of advanced AI/ML infrastructure.
Design and implement distributed and scalable AI/ML pipelines.
Deploy and monitor AI models in production environments with real-time observability.
Apply MLOps principles for automation, CI/CD, and reproducibility.
Implement security, governance, and compliance in AI/ML workflows.
Explore emerging trends like federated learning, LLM infrastructure, and sustainable AI.
Prerequisites
Strong understanding of AI/ML workflows and experience with frameworks such as TensorFlow or PyTorch.
Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Experience with DevOps/MLOps tools like CI/CD, Terraform, or MLFlow.
Basic knowledge of distributed systems and IT infrastructure principles.
