Automating the workflow from data preparation to model training.
An ML Pipeline is an end-to-end workflow that automates the sequence of steps required to build and run a machine learning model. In a simple project, you might run each step—data loading, preprocessing, training, evaluation—manually from a script. However, in a production environment, this is not scalable or reliable. A pipeline formalizes these steps into a directed acyclic graph (DAG), where each step is a node, and the output of one step becomes the input to the next. A typical pipeline might look like this: Data Ingestion -> Data Validation -> Data Preprocessing -> Model Training -> Model Evaluation -> Model Deployment. The benefits of using pipelines are numerous. They enforce a logical separation of concerns, making the code more modular and easier to maintain. They ensure Reproducibility; since the entire workflow is codified, you can be sure that running the pipeline again with the same data and code will produce the same result. They also facilitate Automation. Pipelines can be scheduled to run automatically, for example, to retrain a model every night on new data. Tools like Scikit-learn's `Pipeline` object allow you to chain together preprocessing and modeling steps within your code. For more complex, production-grade workflows, specialized tools like Apache Airflow, Kubeflow Pipelines, or the pipeline features within cloud ML platforms are used to orchestrate these multi-step processes. Adopting a pipeline-based approach is a key step in moving from experimental ML to production-ready ML.