Building a successful cloud-based A.I. dev 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.

With years of experience building , Simon will walk through how the team at PredictionIO (acquired by Salesforce) navigated this roadmap and he will share the on-going challenges ML SaaS platforms face when developers try to build customized large-scale predictive applications on production environments.

Specifically, Simon will walk through these topics:

• The ML platform product design dilemma 
• The difference between building A.I. in a lab and for real applications on production 
• The uniqueness of several predictive applications
• The steps to build an end-to-end Machine Learning solution, from data sourcing to live monitoring
• The future of A.I. dev platforms