MLOps: Operationalizing AI

Catalog description

Examines the machine learning operations, or MLOps, life cycle for designing, deploying, and managing scalable machine learning systems. Surveys emerging trends, such as serverless computing and edge deployments, to illuminate future directions in MLOps. Highlights foundational methods for data ingestion, pipeline orchestration, and performance monitoring while addressing ethical and security considerations. Emphasizes cross-functional collaboration between data science and engineering for production-ready solutions that prioritize reliability and reproducibility. Demonstrates how automation strategies and best practices accelerate feedback loops and streamline model updates. Encourages a practice-based approach that integrates experimental results with continuous delivery to align with organizational goals.