5 Challenges To Be Prepared For Before Scaling Machine Learning Models

Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives. Though gaining worldwide acclaim, ML models are hard to be translated into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production.