As companies adopt AI/ML, they run into operational challenges with cost and ROI questions…how do you capture the costs of featurization, training and predictions? How do you forecast the costs? If a model needs an expensive GPU, what’s the ROI on a model or a set of features?
At Intuit, the ML platform is responsible for the different parts of the Model Development Lifecycle(MDLC) from Feature Eng to Training and to Model Hosting. The platform team has been able operationalize cost transparency and cost chargeback to the lines of businesses.
In this session we will give a brief overview of the Intuit’s ML platform, with a specific focus on operational cost transparency across feature engineering, model training and hosting. We will also talk about the benefits we’ve observed.