Model Operationalization
Model operationalization is today’s biggest hurdle for data science. Sometimes called the “last mile” for analytics, this is where data science meets production IT. And it’s where business value is (or is not) created. Wavicle’s data science model operationalization services help clients achieve their vision of becoming a model-driven business that deploys and iterates models at scale.
Wavicle’s data science consulting’s proof of value is in ModelOps, the analogue of DevOps. Our dedicated team of data science ML operationalization consultants move models from the lab to production, manage and scale models to meet your enterprise’s demand, and monitor them to drive continuous improvement. Our ModelOps follow DevOps ground rules to ensure IT compliance, security, and manageability. This service is a must-have for predictive analytics at scale, where model comparisons and deployment are continuous.
In contrast to the system development life cycle (SDLC) which is defect-driven and deterministic, Wavicle’s model development cycle is heuristic, i.e., continuously iterative and constantly evolving. Using ever-progressing best practices that span clients’ training and inference environments (production data), Wavicle’s data science model operationalization services provide for develop-and-train models (package model, certify model, deploy model, serve model, monitor model, iterate/deprecate model) that can accurately predict, refine, and validate your business data for subsequent operationalizing ML processes.
Contact our consultants to learn more about how your business can operationalize promising ML solutions in marketing and sales, customer service and support, competitive intelligence analytics, RPA, workplace safety and compliance, predictive analytics, ERP metrics, and much more.
