An ML pipeline coordinates tasks such as data preparation, training, evaluation, validation, and deployment. Instead of repeating notebook steps by hand, an orchestrator runs connected components with the data and compute they need. The workflow can loop: poor evaluation results may send work back to model design or data preparation. section 1.1.1 section 5.2
Build the workflow in stages:
- Make each task a reusable component with a defined responsibility. A data preparation pipeline can connect dataset download to training, test, and validation splits. section 7.3 section 5.2
- Add training and validation components. Plan how they access data, store model outputs, and expose metrics; assuming local files will be available in an automated run can cause trouble. section 8.0 section 8.2
- Compile and connect components in Kubeflow Pipelines (KFP). Parameterize runs where needed; a larger workflow can also trigger another pipeline. section 9.3.6
From preparation to a recommender model
Start with separate download and dataset-splitting components. A larger recommender training pipeline then brings together tasks for data checks, negative sampling, dataset metadata, training, validation, and promotion to staging. Its configurable inputs include batch size, learning rate, and training epochs; MLflow and MinIO manage models and data. section 7.3 section 9.3.6
In development and production, CI or a programmatic call can trigger the automated sequence. Deployment adds a serving service after validation; monitoring can trigger another run when performance falls below a threshold. For continuous retraining, data and model validation, lineage, metadata, and pipeline monitoring become important safeguards. section 1.1.2 section 2.5.2
Not every pipeline needs the same shape. Sequential chains suit straightforward retrieval-augmented generation workflows; conditional branches, parallel work, and refinement loops call for graph-based workflows. Choose the structure to match the dependencies and iteration the application actually needs. section 12.1.3 section 12.5
