Toptal helps food and beverage leader cut machine learning infrastructure costs by more than 98%.
A global food and beverage leader partnered with Toptal to modernize its marketing analytics platform, dramatically reducing infrastructure costs while accelerating marketing investment analysis across 26 global markets.
Client
A global food and beverage company producing snacks and drinks sold across international markets.
Employees
Revenue
Industry
Consumer Packaged GoodsDelivered Services
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Schedule a CallChallenge
The company’s legacy machine learning (ML) infrastructure struggled to efficiently process its growing volume of marketing modeling scenarios, resulting in slow analytical turnaround times, high infrastructure costs, and limited scalability across global markets.
Solution
Scalable ML Architecture
Toptal redesigned the platform as a distributed machine learning system capable of executing hundreds of thousands of model configurations in parallel, with elastic compute orchestration dynamically scaling infrastructure based on workload demand.
Enterprise MLOps Foundation
Toptal introduced model versioning, experiment tracking, automated testing, CI/CD pipelines, and infrastructure-as-code while developing high-performance APIs and near real-time analytics capabilities.
Outcome
Significant Cost Reduction
Daily ML pipeline operating costs fell from more than $5,000 to less than $80, reducing infrastructure spending by more than 98% while the platform generated hundreds of times more model outputs.
Faster Insights Across 26 Markets
Analytical turnaround time dropped to under two hours, while the platform enabled near real-time processing of hundreds of millions of model output data points and supported marketing investment analysis across 26 global markets.
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