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.
Primary Features
- HPO via Bayesian Optimization
- Multi-model failover (LightGBM, SVR, Elastic Net)
- Automated ML Lineage with DVC & Prefect
- CI/CD Integration with Snyk security gates
- Low-latency caching with ElastiCache Redis
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.
Sales volume
Demand curve
Results
Architected by Kuriko IWAI
Continue Your Learning
If you enjoyed this blog, these related entries will complete the picture:
Building an Automated CI/CD Pipeline for Serverless Machine Learning on AWS
Building a Serverless ML Lineage: AWS Lambda, DVC, and Prefect
Related Books for Further Understanding
These books cover the wide range of theories and practices; from fundamentals to PhD level.
Linear Algebra Done Right
Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Share What You Learned
Kuriko IWAI, "Bayesian Demand Modeling & Production MLOps" in Kernel Labs
https://kuriko-iwai.com/labs/bayesian-demand-modeling-and-mlops
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Written by Kuriko IWAI. All images, unless otherwise noted, are by the author. All experimentations on this blog utilize synthetic or licensed data.