Applied Data Science: Building Scalable Model Pipelines

Transitioning from a local notebook to a production-ready, scalable data pipeline is one of the biggest challenges in modern data science. This course bridges that gap by introducing you to the core principles of designing and running robust predictive workflows in the cloud. You will transform from a local analyst into a practitioner capable of structuring clean, reproducible data pipelines. You will gain a deep understanding of how to ingest data, manage features, train models, and deploy them using modern containerization and workflow orchestration techniques. What you will learn: 1. Understand the foundational architecture of scalable data pipelines and the role of modern MLOps. 2. Process large datasets efficiently using modern dataframe libraries like Polars. 3. Build reproducible data preprocessing and feature engineering pipelines. 4. Containerize your machine learning workflows using Docker for consistent environment deployment. 5. Configure automated model training and evaluation steps within a cloud-ready architecture. 6. Apply best practices for model tracking and version control. The course begins with essential definitions and pipeline architecture concepts before guiding you through hands-on, text-based implementation exercises. You will learn to write clean, modular Python code that scales seamlessly from your local machine to cloud environments. This course is designed for aspiring data scientists, software engineers, and analysts who want to transition from basic scripting to building robust, production-grade workflows. No advanced engineering background is required, as we start with foundational concepts and step-by-step written explanations. Start building reliable, cloud-ready data pipelines today.