Designing and Deploying ML Pipelines on Cloud Platforms

Transitioning a machine learning model from a local notebook to a reliable, automated production system requires robust pipeline design. This text-based course guides you through the core principles of building, deploying, and managing scalable ML pipelines on modern cloud platforms. You will transition from manual model training to fully automated workflows, mastering the patterns needed to orchestrate data ingestion, preprocessing, training, and deployment. You will gain a deep understanding of MLOps best practices, ensuring your models remain accurate, reproducible, and easy to maintain over time. What you'll learn: - Understand foundational ML pipeline concepts and the core stages of MLOps - Design automated data ingestion and preprocessing workflows on cloud infrastructure - Orchestrate training jobs using Kubeflow and Vertex AI pipelines - Implement continuous integration and continuous delivery (CI/CD) for machine learning models - Configure model monitoring to detect data drift and performance degradation - Practice managing metadata and artifact lineage for complete reproducibility. The course begins with essential terminology and pipeline architecture before moving into step-by-step written guides on orchestration, automation, and cloud integration. This course is designed for beginner data scientists, software engineers, and aspiring MLOps professionals looking to scale their machine learning projects; no prior cloud pipeline experience is required. Start reading today to transform your local ML scripts into production-ready automated pipelines.