DeveloperWeek NYC 2018

The promises of AI are great, but taking the steps to build and implement AI within an enterprise is challenging. As companies learn to build intelligent products in real production environments, engineering teams face the complexity of the machine learning development process—from data sourcing and cleaning to feature engineering, modeling, training, deployment, and production infrastructure. The secret behind enterprise AI success is to master an underlying platform that accelerates AI development at scale for both internal and external data scientists. Often, this task is easier said than done. Navigating the process of building a platform bears complexities of its own, particularly since the definition of “platform” is broad and inconclusive. Based on years of experience helping enterprises establish AI product strategies, Dr. Simon Chan walks through the various stages of building an AI platform that is right for your business, and avoiding common pitfalls. Dr. Chan shares his executive experience building unified AI platforms to power advanced machine learning, deep learning, natural language processing, and smart data discovery for multiple enterprise product lines. He discusses the strategic challenges involved in scaling a stable production AI system, engineering reusable components for repeatable success, automating AI with AI to accelerate adoptions and empowering developers with an expandable ecosystem. Along the way, he shares best practices for building an AI platform for large-scale production deployment.