Structured data organizes machine-readable fields into a consistent format that production ML pipelines can collect, transform, validate, split, and store. Tabular data is especially useful because it is already numerical or can be converted into numerical features. section 1.4.2 section 9.3
A production workflow uses structured data to:
- Save prepared datasets as Parquet files and place them in a data lake or MinIO bucket. section 9.3
- Create train, test, and validation splits before model training. section 9.3
- Run data-quality checks before training and define validation metrics for model assessment. section 9.3
- Support feature stores, drift detection, model testing, observability, and experiment tracking. section 1.4.2
Movie recommendation pipeline
A MovieLens-based recommender collects structured data, saves it as Parquet, stores it in MinIO, creates dataset splits, checks incoming quality, trains a model, and tracks experiments and model versions with MLflow. section 7.0 section 9.3
Figure 7.1 The mental map where we’re now focusing on the first step/component of our pipeline—data collection and preparation (3)
Use the pipeline mental map to locate structured data within the collection and preparation stage that feeds later training and validation. section 7.0
