Pipelines and Workflow Automation
ML workflows typically take on a linear progression of sequential steps (although most production applications require several additional steps to create a cyclical pattern for model monitoring, continuous training, and CI/CD stages found in Machine Learning Operations (MLOps)). Pipelines in scikit-learn provide a structured way to automate machine learning workflows by chaining together multiple processing steps such as data preprocessing, model training, and prediction into a single, cohesive object. This allows for efficient and consistent execution of complex workflows while ensuring that each step, from transformation to prediction, is executed in the correct sequence.
MLOps
MLOps refers to the practice of integrating ML workflows into the larger lifecycle of software development and operations. It focuses on automating the process of developing, testing, deploying, and maintaining ML models, ensuring they are scalable, reliable, and sustainable...
