Practicing data science comes with challenges. It comes with fragmented data, a short supply of data science skills, and various tools, practices, and frameworks to choose from run with rigid IT standards for training and deployment. It’s also challenging to operationalize ML models with unclear accuracy and difficult-to-audit predictions.