A machine learning pipeline is a defined sequence of steps that turns raw data into a deployable model: collecting and cleaning data, preparing features, training, evaluating, deploying and monitoring. Automating these steps makes results reproducible and lets new data be processed with little effort. A retailer, for instance, can retrain and test its sales forecasting model every week using the latest figures.
Data cleansing is the detection and correction of incorrect, duplicate or inconsistent entries in data sets. Typical steps include standardising spellings and date formats, removing superfluous spaces and merging duplicates such as 'Smith Ltd' and 'Smith Limited'. Clean data is a prerequisite for reliable analyses, mailings and AI applications.
Feature engineering means selecting, transforming and creating attributes (features) from raw data so that a learning algorithm can detect patterns more easily. Feature extraction derives compact, meaningful values from complex data such as text, images or signals. Example: individual purchase records are turned into features like ‘days since last purchase’ or ‘average order value’, which can noticeably improve a churn prediction.
MLflow is an open-source platform for managing the entire life cycle of machine learning models, originally introduced by Databricks in 2018. It records experiments with their parameters and metrics, manages model versions in a registry and simplifies deployment. More recent versions also support tracing and evaluating applications built on language models. Teams can therefore trace which model was produced with which data and settings.
Kubeflow is an open-source platform for running machine learning workflows on Kubernetes, from data preparation through training and hyperparameter search to model serving. Originally initiated by Google, the project belongs to the Cloud Native Computing Foundation, where it reached “graduated” status in August 2026 (as of October 2026). It is aimed mainly at teams that train their own models at scale on their own or cloud infrastructure.
