The PYTHON531-PYTHON534 Machine Learning Operations (MLOps) series provides an essential foundation for understanding the operations and practices that bridge machine learning development with reliable deployment and maintenance. Beginning with the background of current machine learning operations, we explore the challenges organizations face in scaling machine learning solutions, including issues in reproducibility, monitoring, and collaboration across teams. Central to this series of courses is the exploration of MLOps practices, covering the full machine learning lifecycle. We also outline the key roles involved in successful MLOps implementations, including data scientists, machine learning engineers, and data engineers. By the end of this module, learners will gain a comprehensive understanding of how MLOps enables scalable, reliable, and efficient machine learning operations within modern organizations.
In PYTHON534 - Deploying with MLFlow you'll learn about the deployment of MLflow models and about different tools you can use to manage the deployments.
We'll also explore MLflow’s Recipes component, a framework designed to simplify and standardise the development of machine learning pipelines. Starting with the foundational concepts, we cover key elements such as Steps, which represent individual tasks in the ML pipeline; Recipes, which combine steps into reproducible, end-to-end workflows; Templates, which provide predefined pipeline structures for common ML tasks; Profiles, which allow for environment-specific configurations; and Step Cards, which document and track each step’s input, output, and performance. Following the conceptual overview, we will implement a recipe from scratch, guiding them through defining and configuring the steps needed for a complete ML workflow. By running the recipe, they will see firsthand how MLflow Recipes enable consistent, scalable, and organized pipelines suitable for both experimentation and production. By the end of this module, learners will have the practical skills to use MLflow Recipes to build, execute, and manage standardized machine learning workflows, enhancing reproducibility and efficiency in their projects.