Building Machine Learning Pipelines with Kedro, MLflow, and Metaflow

Moving machine learning models from messy experimental notebooks to structured, reproducible pipelines is one of the biggest challenges in data science today. Instead of building custom infrastructure from scratch, you can leverage powerful, ready-to-use tools to manage your data and models. This text-based course guides you through the foundational concepts of machine learning operations (MLOps) using popular off-the-shelf frameworks. You will understand how to structure your data science projects, track experimental metrics, version your models, and orchestrate complex workflows smoothly.\n\nWhat you'll learn:\n- Understand the core principles of machine learning pipelines and modern MLOps workflows\n- Organize and modularize your data science code using the Kedro framework\n- Track parameters, metrics, and model artifacts systematically with MLflow\n- Orchestrate and scale data science workflows from prototype to production using Metaflow\n- Apply best practices for data versioning, reproducibility, and dependency management\n- Compare different pipeline tools to select the right solution for your specific project needs\n\nYou will start with the fundamental terminology of pipeline orchestration before diving into step-by-step written explanations of each tool. The material guides you through structuring data, managing model runs, and establishing reproducible workflows. This course is designed for beginner data scientists, software engineers, and analytical professionals looking to transition into MLOps. No prior experience with pipeline frameworks is required, though a basic understanding of Python is helpful. Start reading today to bring structure, reproducibility, and professional engineering standards to your machine learning projects.