A comprehensive guide to the End-to-End Data Platform architecture, schema enforcement, and automated machine learning pipelines.
In production ML systems, Data Quality is often the bottleneck. Models fail not because of bad algorithms, but because of bad data (drift, missing values, wrong types).
DATAION enforces a strict Data Contract at the ingestion layer. If the data doesn't match the schema, it never reaches the model. This ensures 100% reliability for downstream training pipelines.
Schema-on-write enforcement. Invalid data types or missing required columns are rejected immediately.
Every uploaded file is hashed, versioned, and stored with metadata for full reproducibility.
Integrated Scikit-learn pipeline that automatically encodes categorical features and trains Random Forest models.
Create a new project and define your Data Contract. You can set column names, data types (Int, Float, String), and requirement status. Use the 'Auto-fill from CSV' feature to infer schema instantly.
Upload your raw CSV dataset. The backend engine validates every single row against the schema. If even one required field is missing, the upload is flagged as Invalid.
Visualize column distributions, detect missing values, and inspect sample data to understand your dataset health before training.
One-click training using the AutoML engine. The system handles preprocessing (Imputation, One-Hot Encoding) and training. Finally, download the serialized model (.joblib) for production use.
The platform is built on a high-performance FastAPI backend. Below are the core endpoints exposed by the service.