The solution design and the key questions that one should ask depends on the phase ofmodel evolution. For astandalone model phase the emphasis is more on getting the data from other enterprise data warehouses or data lakes provisioned to the data scientists for them to demonstrate model performance. Using open-source alternatives or pre-packaged models from ML platforms, may be quick ways of determining initial feasibility. For aprediction as a service phase we need to worry about security, how frequently the models need to be retrained and redeployed, and ensuring the traceability of data, models, and software. For amodel factory phase, the emphasis of solution design is much broader; the solution designers need to worry about how multiple groups of people with varying levels of expertise and skills can build, train, test, consume, and monitor models. They need to provide a secure ‘sandbox’ as well as a production infrastructure for multiple groups to operate in. Selection of the righ