Machine learning model development needs to be methodically, with extreme care, in order to yield effective results as unintended consequences can emerge at the various stages of model development. Flexible data ingestion policies allow companies to simplify ML model development by reducing the time needed for the model to teach itself and achieve greater levels of accuracies. However, enterprises are often unable to collaborate on model development as a result of the risk associated with data security in a model developed by multiple stakeholders. Cape Privacy is an encrypted learning platform that allows companies to collaborate on machine learning models without the risk of compromising any proprietary or sensitive data. The data remains encrypted throughout and can be plugged into models seamlessly, allowing data scientists to truly harness the power of machine learning with efficiency and unprecedented speed. AlleyWatch caught up with CEO Che Wijesinghe to learn more about the inspiration for the business, future plans, latest round of funding from investors that include Evolution Equity Partners, Tiger Global Management, Version One Ventures, Haystack, Radical Ventures, Ridgeline Partners, and Boldstart Ventures.