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 the model monitoring, continuous training, and continuous integration/continuous delivery or deployment (CI/CD) stages found in machine learning operations (MLOps)—more on this later in this book). In scikit-learn, pipelines provide a structured way to automate ML 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 life cycle 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 in production environments. MLOps is essential in a production environment for several reasons:
1) It bridges the gap between data science, ML engineering, and operational teams so that there is less of a “this is your job, this is our job” mindset between them
2) It improves collaboration since teams must think holistically about how models are utilized from various vantage points
3) It speeds up model deployment by creating an ecosystem that automates pipeline tasks and maintains a framework for easy reproducibility across projects
4) It enhances model performance monitoring, observability, and explainability to address issues such as model drift or technical debt
MLOps is crucial for businesses that rely on ML models to drive decision-making and automation, as it ensures that models are consistently performing at their best even after deployment. It enhances reproducibility and traceability, both of which are key for compliance, auditing, and continuous improvement. By employing MLOps, organizations can build efficient workflows for retraining models, managing datasets, and monitoring real-time model behavior, which minimizes disruptions and reduces risks associated with outdated or underperforming models. Remember, there is “No such thing as a free lunch” and, equally, “There is no such thing as a model that works well forever!”
scikit-learn supports MLOps workflows through tools such as the Pipeline() class for automating preprocessing and modeling steps, GridSearchCV() for hyperparameter optimization, and model persistence libraries such as joblib and pickle for saving and deploying models. Additionally, scikit-learn’s compatibility with other MLOps platforms ensures that models built with it can be integrated into larger ML life cycle systems such as MLflow or Kubeflow.
In Chapter 14, we will demonstrate how to create pipelines that include transformers such as ColumnTransformer() and estimators such as RandomForestClassifier() to streamline data preprocessing, model selection, and cross-validation into a unified process. By encapsulating this workflow, pipelines help eliminate manual intervention and make your ML process more reproducible. Furthermore, this encapsulation process is tightly bound to the scikit-learn paradigm of modularity, which makes creating a custom library of functions, pipelines, estimators, and transformers easy.
