We're sunsetting PodQuest on 2025-07-28. Thank you for your support!
Export Podcast Subscriptions
cover of episode DIY With AI: The Home Depot's Huiming Qu

DIY With AI: The Home Depot's Huiming Qu

2021/5/25
logo of podcast Me, Myself, and AI

Me, Myself, and AI

AI Deep Dive AI Chapters Transcript
People
W
Wei-Ming Ke
Topics
Wei-Ming Ke:我在Home Depot带领数据科学团队,致力于改进客户的在线购物体验,解决诸多挑战性问题,例如在拥有超过200万种产品的庞大数据库中,有效地训练机器学习算法,从而为客户提供可扩展的个性化推荐。解决客户痛点需要跨职能团队(数据科学家、用户体验设计师、产品工程师)的合作,共同制定目标。Home Depot利用AI提供项目指南推荐,并根据项目难度进行分类,实时识别客户正在进行的项目。Home Depot关注客户的整个购物旅程,利用AI记住客户之前的搜索、购物车内容、访问记录等信息,提供更个性化的服务。为加快项目进度,Home Depot采用迭代式开发方法,先开发轻量级测试版本,再逐步完善。Home Depot的员工普遍相信机器学习和AI的力量,并强调人机协作的重要性,因为在居家装修领域,专业知识至关重要。未来,数据科学领域需要更多具备跨学科能力的人才,能够连接不同领域的数据和算法,而非仅仅专注于算法本身。成功的关键在于如何有效地整合数据、构建可扩展的解决方案,而非一味追求算法的改进。成功的AI应用更注重数据整合和价值创造,而非单纯追求算法效率的微小提升。我的职业生涯充满了偶然性,但我通过不断学习和尝试,积累了丰富的经验,最终在Home Depot找到了理想的工作。从学术研究转向商业应用,最重要的转变是奖励机制的变化,研究人员的奖励在于发表论文和专利,而商业应用则更注重产品落地和快速迭代。团队的成功至关重要,我致力于创造一个鼓励创新、实现科学梦想的环境。 Sam Ransbotham: 对Wei-Ming Ke的热情和对问题的深刻理解印象深刻,特别是她对奖励机制的调整以及不同反馈循环的建立。 Shervan Kodabande: 赞同Sam的观点,并强调了将解决方案推向生产环境的重要性,以及在算法效率和数据整合之间的平衡。

Deep Dive

Chapters
Huiming Qu discusses how The Home Depot uses AI to assist customers with complex home improvement projects, focusing on search optimization, product recommendations, and real-time personalization.

Shownotes Transcript

Huiming Qu didn’t plan to work in data science, a nascent field at the time she was pursuing a Ph.D. in computer science, but one course in data mining changed all of that. She started her career in the research department at IBM, transitioned to a 50-person startup, spent some time in the financial services industry, and today leads data science and machine learning in the marketing and online functions at The Home Depot.

In this episode, Huiming explains the similarities and differences between her previous experiences and her current role, in which she is tasked with helping customers more easily find the products and services they need as they embark on home improvement projects. (And who hasn’t started at least one of those since the COVID-19 pandemic shifted many of us to working from home?) She also outlines some of the challenges of managing a data set of over 2 million product SKUs and getting pilot programs to market quickly, and she explains why she champions the need for cross-functional teams to execute complex technology projects. Read the episode transcript here).

 

Read more about our show and follow along with the series at https://sloanreview.mit.edu/aipodcast).

 

Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.

Your reviews are essential to the success of Me, Myself, and AI. For a limited time, we’re offering a free download of MIT SMR’s best articles on artificial intelligence to listeners who review the show. Send a screenshot of your review to [email protected]) to receive the download.

Guest bio:

Huiming Qu leads the online data science and platform team enabling search, product recommendations, real-time personalization, visual shopping, and various other innovations for The Home Depot’s digital channels. She is a technical leader with deep expertise in artificial intelligence, data science, engineering, and product leadership, with a proven record of driving billion-dollar contributions with scalable machine learning solutions and strategic innovation. Qu has more than 10 years of experience managing large AI and data science programs at IBM’s Watson research lab, Distillery, and American Express. She earned a Ph.D. in computer science from the University of Pittsburgh; holds six patents and has others pending approval; and has published more than a dozen academic papers around data management, machine learning, and optimization.

We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.

We want to know how you feel about Me, Myself, and AI. Please take a short, two-question survey).