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Crossing the enterprise AI chasm

This will be presented in English.

Simon Chan (Salesforce)
11:1511:55 Thursday, April 12, 2018
企业人工智能 (AI in the Enterprise), 英文讲话 (Presented in English)
Location: 紫金大厅B(Grand Hall B) Level: Beginner
Secondary topics:  设计AI平台(Designing AI platforms)

您将学到什么 (What you'll learn)

了解AI研究与真实AI应用之间的鸿沟。

了解实施AI产品计划的注意事项。

Understand the the chasm between AI research and real AI applications

Learn considerations for implementing AI product plans

描述 (Description)

本讲话将用英语授课,同时会提供中文同声传译。中文版本摘要会在英文摘要下面给出。

Building an end-to-end AI application in production is tremendously more complicated than simply doing algorithm modeling in a lab. Simon Chan explains how to cross the gap between AI research fantasy into real-world applications. Drawing on his experience building the PredictionIO and Salesforce Einstein platforms, Simon details how enterprises navigate the end-to-end machine learning application development journey and shares the ongoing challenges that cloud-based AI dev platform providers face when they try to enable developers to build large-scale customized predictive applications in production environments.

In particular, Simon covers the 10 steps to bring AI into production:

  1. Define the prediction use case
  2. Decide on the intelligent app presentation
  3. Import the right data
  4. Construct potential features for modeling
  5. Specify labels
  6. Find the right model
  7. Set evaluation metrics
  8. Serve predictions
  9. Collect feedback for improvement and evaluation
  10. Keep monitoring and be crisis ready

构建一个端到端的AI应用并部署到生产系统上远比在实验室里简单地做算法建模要复杂得多。尽管很多企业已经在AI研究上投资很多,它们依然面临着把看起来很了不起的研究结果真正地用于实战的挑战。受创业社区的“跨越鸿沟”理论的启发,Simon将会系统地分析这一特别的问题(“跨越AI鸿沟”),即如何跨越多个步骤的距离,把AI引入真实应用。

基于他在构建PredictionIO和Salesforce的Einstein平台的经验,Simon将会一步一步地介绍企业如何完成端到端机器学习应用开发的全过程。从多个视角出发,他还会分享现有的基于云的AI开发平台提供商会面临的挑战,如果这些提供商试图去附能开发人员在生产环境里构建大规模定制化的预测应用。

具体地,Simon将会介绍把AI引入真实业务场景的生产系统所需的10个步骤:

1. 定义预测的业务场景

2. 确定智能App的表现

3. 导入正确的数据

4. 为模型构建潜在的特征

5. 指定标注数据

6. 找到正确的模型
7. 设置评估指标

8. 进行预测

9. 为改进和评估收集反馈
10. 持续监测并为危机做好准备

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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Comments

Alexey Medvedev | DATA SCIENCE EXPERT
2018-05-03 01:25 CST

Dear Simon

thanks for the great presentation.

Would you mind to share the slides?

Thanks in advance
Alexey