Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

The journey of machine learning platform adoption in enterprise

Simon Chan (Salesforce)
14:5515:35 Thursday, 24 May 2018
Data-driven business management, Strata Business Summit
Location: Capital Suite 15/16 Level: Non-technical
Secondary topics:  Data Platforms, Managing and Deploying Machine Learning

Who is this presentation for?

  • Product leaders, managers, and executives

What you'll learn

  • Learn best practices for enabling AI products with a platform built for large-scale production deployment
  • Understand the challenges, as well as various solutions, 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

Description

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, Simon Chan walks you through the various stages of building an AI platform that is right for your business while avoiding common pitfalls. Simon 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 and 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 outlines best practices for building an AI platform for large-scale production deployment.

Photo of Simon Chan

Simon Chan

Salesforce

Simon Chan is a senior director of product management for Salesforce Einstein, where he oversees platform development and delivers products that empower everyone to build smarter apps with Salesforce. Simon is a product innovator and serial entrepreneur with more than 14 years of global technology management experience in London, Hong Kong, Guangzhou, Beijing, and the Bay Area. Previously, Simon was the cofounder and CEO of PredictionIO, a leading open source machine learning server (acquired by Salesforce). Simon holds a BSE in computer science from the University of Michigan, Ann Arbor, and a PhD in machine learning from University College London.

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