In a forecasting pipeline, the model is only one stage. The contracts between preparation, evaluation, prediction, and export determine whether the result can be trusted.

Context

At Datalysis Group, I contributed to a forecasting workflow implemented in Python and orchestrated with Mage AI. My work covered components across data preparation, feature engineering, model training and evaluation, prediction, alerts, and data-platform-facing exports.

The pipeline interacted with PostgreSQL and Snowflake-related paths. This case study focuses on the code and contracts I worked on inside that workflow.

Engineering contribution

I implemented and corrected pipeline components that transformed source data, constructed model inputs, evaluated forecasts, produced predictions, and prepared outputs for downstream systems.

Several important fixes were not algorithm changes. They concerned the units used during evaluation, output schemas, write behavior, and duplicate exporters. Keeping predictions separate from evaluation metrics made the downstream contract clearer and reduced the chance that one output path would overwrite or confuse another.

I also documented data contracts, the model workflow, and scheduled outputs so future changes could be reasoned about from more than the orchestration graph alone.

PIPELINE CONTRACT MAP / 01One pipeline, two output contracts.Evaluation describes model behavior. Predictions feed operational paths. Keeping them separate protects meaning downstream.
  1. 01PrepareSource data and declared unit
  2. 02FeaturesModel-ready inputs
  3. 03TrainFit the forecasting model
  4. 04EvaluateMeasure on the correct unit
  5. 05PredictProduce scheduled forecasts
CONTRACT FORK
OUTPUT / AEvaluation metricsEvidence about model behavior; not a prediction table.
OUTPUT / BPrediction recordsSchema-bound outputs for alerts, databases, and downstream consumers.
  • Declared evaluation unit
  • Explicit output schema
  • Declared write and rerun behavior
  • Documented ownership

Why the boundaries mattered

Evaluation needs a declared unit

A metric is ambiguous if it is unclear whether rows represent transactions, products, locations, or time windows. Correcting the evaluation unit was necessary before interpreting model performance.

Exports are interfaces

Database writes are not an implementation afterthought. Schema, keys, write policy, and idempotency determine whether a scheduled pipeline can be rerun safely and whether downstream consumers receive the meaning they expect.

Orchestration does not replace contracts

Mage AI made execution order visible, but the reliable part of the workflow still depended on explicit inputs, outputs, validation, and documentation at each stage.

Takeaway

The work reinforced a systems lesson I now apply beyond forecasting: reliability often comes from fixing the seams around a model, including the units, schemas, writes, and ownership boundaries that make its output usable.