Industry
CPG
Region
North America
Solution
Unified MLOps Framework
Context
Advancements in data science and machine learning (DS and ML) have expanded the scope of use cases across industry-wide business verticals. However, the absence of a unified approach, the presence of repetitive steps within each project, and the lack of reproducibility have negatively impacted the projects’ timelines, causing many of the data science projects to fail. The emergence of machine learning operations (MLOps) has allowed companies to integrate their ML infrastructure better and helped engineers and data scientists accelerate the path to production while maintaining high standards and lower costs.
Problem statement
The client is a leading beverage manufacturer and distributor in North America with operations spread across the continent. They had been working with MathCo to build use cases in DS and ML to optimize their business functions and processes. While we used industry-standard practices for executing each of the DS and ML projects, we realized that a number of steps were repeating for each implementation, making it a time- and cost-intensive process.
Impact
- Created awareness of the best practices in MLOps
- Introduced modularity and reusability in DSML project implementation
- Accelerated efficient launch of ML models
- Calculated an approximate 20% Infra cost savings for every use case
- Estimated 30% reduction of onboarding time for projects by utilizing reusable components