• Define business goals and potential use cases.
• Identify gaps between “data” and “action.”
• Translate business use cases into ML problems and ML solutions.
• Build ML models to make predictive decisions.
• Design, train and test ML models using cutting-edge model architecture and tools.
• Gain transformative positive effect of models in action.
• Integrate model outputs with various business-critical applications.
• Achieve data integration and model inference at scale.
• Operationalize production-ready ML through MLOps.
• Ensure ongoing consistency and repeatability of ML solution.
• Track solution performance, monitor alerts, refresh model.
• Feed future model building with solution automation.
TechCelerated’s data scientists helped Schnucks develop a machine learning model to predict shopper likelihood to buy certain products. The 12-week proof of concept gave the Schnucks team the opportunity to learn the platform and absorb TechCelerated’s analytic approach in a collaborative environment.