Building an A.I. Cloudy Sky: What We Lea...

Building a successful A.I. cloud platform on top of an open-source Machine Learning project is more complicated than one would imagine. Simply offering a hosted version of the project is hardly the answer. The secret to success is to differentiate the needs between the open-source users and the potential SaaS users. Oftentimes, they are of different species. We will walk through how PredictionIO navigates the roadmap. Building a software-as-a-service business on top of an open-source project involves more than just providing a hosted version of it. It is a little-known fact that people in the existing open-source user segment are unlikely to become the potential cloud product customers. To productize an open-source project as a cloud platform, understanding who the customers are and what they are willing to pay for are the keys. Business techniques like customer discovery and lean product development can be applied to increase the chance of success. At the same time, keeping the open-source project and the cloud product under one umbrella while developing them separately for different user segments is non-trivial. We'll dive into some real scenarios, both successful and not so successful ones, of how we navigate the cloud product roadmap based on the popular open-source Machine Learning server project -- PredictionIO. Specifically, we will walk through these topics: • Evaluating Different Cloud Approaches • Understanding Existing and Potential Users • Finding the Unique Values of the Cloud • Contributing Back From the Cloud to the Open-Source