MLOps and Responsible AI Practices

Bringing generative AI models from development to production requires more than just clean code; it demands robust infrastructure and ethical guardrails. This text-only course guides you through the essential principles of Machine Learning Operations (MLOps) combined with responsible AI practices. You will transition from building experimental models to establishing reliable, secure, and fair AI systems in production environments. What you will learn: 1. Understand the core foundations of MLOps pipelines and lifecycle management. 2. Implement responsible AI frameworks to detect bias and ensure model fairness. 3. Deploy generative AI models using modern CI/CD and cloud infrastructure. 4. Monitor model performance, data drift, and ethical alignment in real-time. 5. Apply retrieval-augmented generation (RAG) patterns safely within production workflows. 6. Configure governance and compliance standards for enterprise AI deployments. Starting with key definitions and foundational concepts of machine learning operations, this course walks you through pipeline construction, deployment strategies, and continuous monitoring. It is designed for beginners, data scientists, and technical professionals looking to understand production AI without needing advanced deployment experience. Start your journey toward building reliable and ethical AI systems today.