Design scalable enterprise AI architecture that takes AI from isolated prototypes to governed, secure production systems. Explore how data pipelines, cloud-native infrastructure, MLOps, and deployment controls work together to support reliable enterprise AI delivery.

Designed for AI/ML engineers, architects, and technical leaders, this course examines enterprise AI design principles, scaling bottlenecks, governed ELT pipelines, managed machine learning services, infrastructure as code, and high-throughput compute and storage. You'll evaluate dataset snapshots, artifact linking, and model versioning for reproducibility and rollback. Explore hyperparameter optimization, batch inference, job orchestration, and shadow and canary deployments to assess model operations and release strategies. Learn to apply prompt versioning and automated testing principles alongside zero-trust architecture, training data provenance, data poisoning mitigation, AI red teaming, and threat modeling. What makes this course distinctive is its end-to-end architectural focus: connecting infrastructure and data governance with model operations, prompt management, and AI security. By the end, you'll be able to evaluate architecture trade-offs, identify integration risks, and recommend controls for scalable, reproducible enterprise AI systems.