How to Build a Machine Learning System on Serverless Architecture
SMRTR summary
A retailer's shelves hold thousands of products, each awaiting the perfect price tag. For smaller companies battling retail giants like Amazon, that decision is increasingly powered by artificial intelligence.
"Our goal is to leverage AI models to recommend the best price for a selected product to maximize sales for the retailer," explains a new system designed to level the playing field for mid-sized stores.
The serverless architecture deploys multiple reinforcement models, including a primary multi-layered feedforward network built on PyTorch, with several backup models ready when needed. Training on approximately 500,000 samples, the system applies logarithmic transformations to help models better learn pricing patterns.
When a retailer needs guidance, the system springs to life through AWS Lambda functions, pulling trained models from cloud storage and delivering pricing recommendations via API endpoints.
What appears on the retailer's screen is deceptively simple: a visualization showing projected sales across different price points, with an optimal recommendation highlighted.
For smaller retailers competing against algorithmic pricing behemoths, this serverless approach offers enterprise-level intelligence without the enterprise-level investment.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
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