Wayfair Director of Machine Learning Tulia Plumettaz

How AI Happens

Wayfair uses AI and machine learning (ML) technology to interpret what its customers want, connect them with products nearby, and ensure that the products they see online look and feel the same as the ones that ultimately arrive in their homes. With a background in engineering and a passion for all things STEM, Wayfair’s Director of Machine Learning, Tulia Plumettaz, is an innate problem-solver. In this episode, she offers some insight into Wayfair’s ML-driven decision-making processes, how they implement AI and ML for preventative problem-solving and predictive maintenance, and how they use data enrichment and customization to help customers navigate the inspirational (and sometimes overwhelming) world of home decor. We also discuss the culture of experimentation at Wayfair and Tulia’s advice for those looking to build a career in machine learning.

Key Points From This Episode:

• A look at Tulia’s engineering background and how she ended up in this role at Wayfair.
• Defining operations research and examples of its real-life applications.
• What it means for something to be strategy-proof.
• Different ways that AI and ML are being integrated at Wayfair.
• The challenge of unstructured data and how Wayfair takes the onus off suppliers.
• Wayfair’s North Star: detecting anomalies before they’re exposed to customers.
• Preventative problem-solving and how Wayfair trains ML models to “see around corners.”
• Examples of nuanced outlier detection and whether or not ML applications would be suitable.
• Insight into Wayfair’s bespoke search tool and how it interprets customers’ needs.
• The exploit-and-explore model Wayfair uses to measure success and improve accordingly.
• Tulia’s advice for those forging a career in machine learning: go back to first principles!

Tweetables:

“[Operations research is] a very broad field at the intersection between mathematics, computer science, and economics that [applies these toolkits] to solve real-life applications.” — Tulia Plumettaz [0:03:42]

“All the decision making, from which channel should I bring you in [with] to how do I bring you back if you’re taking your sweet time to make a decision to what we show you when you [visit our site], it’s all [machine learning]-driven.” — Tulia Plumettaz [0:09:58]

“We want to be in a place [where], as early as possible, before problems are even exposed to our customers, we’re able to detect them.” — Tulia Plumettaz [0:18:26]

“We have the challenge of making you buy something that you would traditionally feel, sit [on], and touch virtually, from the comfort of your sofa. How do we do that? [Through the] enrichment of information.” — Tulia Plumettaz [0:29:05]

“We knew that making it easier to navigate this very inspirational space was going to require customization.” — Tulia Plumettaz [0:29:39]

“At its core, it’s an exploit-and-explore process with a lot of hypothesis testing. Testing is at the core of [Wayfair] being able to say: this new version is better than [the previous] version.” — Tulia Plumettaz [0:31:53]

Links Mentioned in Today’s Episode:

Tulia Plumettaz on LinkedIn

Wayfair

How AI Happens

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