KGKumar GauravStaff Data Architect

Blue Yonder (JDA) · 2019 - 2021

Forecasting Data & Model Computation

Data and model-computation pipelines behind Blue Yonder's supply-chain AI: preparing enterprise data, running ML models for demand forecasting and delivering predictions at scale on Azure.

The problem

Forecasts are only as good as the data and compute behind them. Large enterprise datasets had to be prepared, fed to models and turned into predictions on a reliable schedule.

Key decisions

  • Separate data preparation from model computation so each scales on its own.
  • Make every prediction run repeatable and traceable back to its inputs.
  • Ship through fully automated delivery: no manual steps from commit to production.
  • Build on shared platform abstractions rather than per-service tooling.

Outcome

Prediction workloads ran at enterprise scale on a platform where releases across many microservices were fully automated, the delivery standard I have brought to every platform since.

AzurePySparkSynapseMachine LearningCI/CD

Architecture flow

  1. Enterprise sales, supply and calendar data
  2. Ingestion and preparation
  3. Feature computation with PySpark
  4. Model computation
  5. Predictions and forecast outputs
  6. Supply-chain planning applications

More architecture work

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