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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.

Course Duration:

27 hours

Level:

Advanced

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.

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