Bayesian Demand Modeling & Production MLOps

Forecast the optimal pricing of retail goods with a multi-model ensemble.

Scale retail revenue with Bayesian-optimized price elasticity. Features a multi-model ensemble (DLN, LightGBM, SVR), DVC lineage, and AWS Lambda deployment.

Price OptimizationDemand modelingmachine learning systemML systemDeep learningDemand Curve ForecastingRetail MLOpsPyTorchLightGBMAWS LambdaServerless MLData Version Control (DVC)Price Elasticity ModelingBayesian OptimizationML LineageDVCSnykFailover

Primary Features

Multi-model failover system (PyTorch deep learning model, LightGBM, SVR, Elastic Net).
HPO via Bayesian optimization with Optuna.
Low-latency caching with ElastiCache Redis.
Weekly-scheduled ML lineage management with DVC & Prefect.
Automated data drift and fairness/bias testing (SHAP).
CI/CD integration with GitHub Actions, AWS CodeBuild, and Snyk for security scanning.

Playground

SKUs

Note: When you trigger a Start Analysis, you may experience a slight delay up to 10 seconds if the system has been idle, due to a cold start of AWS Lambda architecture.
This playground implements a warmup trigger to pre-initialize the ML runtime, reducing the latency associated with Lambda cold starts. Yet, loading the PyTorch artifact might take up to 3 seconds when the system cannot find the cache stored in the AWS EC.