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Mind the Product

Artificial intelligence (AI)

The AI feature checklist: 21 questions addressing familiarity, fallbacks, and feedback

Product Management

Sunday Rewind: 7 things I’ve learned in my first 6 months as a product manager

B2B product management

Artificial Intelligence (AI) & Machine Learning (ML)

Building Product Teams

Business Strategy

Data Driven Product Management

Digital Transformation

Diversity and Inclusion

Minimum Viable Product

OKRs

Prioritisation

Product Design

Product Research

Product Development Process

Product Ethics

Product Discovery

Product Leadership

Product Management Career

Product Strategy

Stakeholder Management

🌎 <a href="https://www.mindtheproduct.com/world-product-day/" style="color:#FFFFFF;text-decoration:underline;">Happy World Product Day!</a> Be sure to celebrate product, yourself, and your peers today! 🎉

How much are product managers getting paid in 2024?

Leading product ops at Delivery Hero with Koen Dubelaar

Connecting fans to creators at Spotify (Dariusz Dziuk, Product Lead, Spotify)

“You’re not as competent as you think” 5 lessons from Ken Chin on navigating a product career (CPO, Spark Networks)

Everything wrong with the product management job market. And how to fix it

Emily brings nearly 15 years of product management experience to her role as Managing Director for Mind the Product. While she has worked across a variety of industries, she will always have a soft spot for the travel industry where she spent most of her early career building software for airlines and developing a leading consumer travel app, TripCase. Emily is passionate about the craft of product management and loves talking about new ways to make products people love.

Managing Director

Emily Tate

<p>In this ProductTank San Francisco talk <a href="https://www.linkedin.com/in/alexmiller90/">Alex Miller,</a> one-time software engineer in the content understanding team at Yelp, gives us a case study of using machine learning (specifically deep learning) to provide a ranking system that surfaces the most beautiful photos of a business to the top of their page.</p>
<h2>Basic Photo Ranking</h2>
<p>You may know of Yelp as a site where users post reviews of businesses. But Alex tells us that the volume of photos is actually far greater than the number of reviews. Yelp wants to ensure the photos that best represent a business are featured at the top of a business’ page. So how do they do that?</p>
<p>Traditionally, Yelp used basic metadata to rank photos such as user feedback (like and votes), photo attributes (photo size, age), and clicks. While this worked up to a point, the underlying assumptions in the model were flawed. For example, a photo might get a lot of clicks because it was blurry and users wanted to see what it was. Or a photo might get a lot of likes because it was funny. But neither of these would be the best representation of the business.</p>
<h2>Helping the Model to Learn</h2>
<p>Yelp was looking for a model that could better understand the <em>quality</em> of photos: features such as depth of field, contrast, alignment, and other characteristics that are typically present in higher quality photos. So it started using a model called a Convolutional Neural Network (CNNs) that would provide a better predictor that photo would qualify as “beautiful”.</p>
<p>For any well-posed machine learning problem, Alex explains that you need a model and data to train that model. Yelp had to find “gold set” of photos that it could reliably say were good and bad to help the model learn. The content understanding team found a variety of signals that helped to determine a proxy: if a photo was taken by a DSLR camera, it would be labeled as “good”. If it was a non-DSLR camera, it would be labeled as “bad”. This allowed the model to learn the characteristics it was looking for as Yelp built its quality score.</p>
<h2>Putting the Model to Use</h2>
<p>Now that Yelp had a fairly reliable predictor of photo quality, the content understanding team had to decide how to use it in practice. Alex talks about other considerations they had to take into account: daily batch vs. streaming evaluations of photos, APIs that would be needed by other teams to use the ranking, and filtering and diversification logic to ensure the ranking would be a feed that would best represent a business on Yelp. In the end, they were able to evaluate the results with metrics and provide an impressive improvement in their feeds using a deep learning approach to evaluate photos.</p>

core/freeform

AI ethics advice from former White House technologist – Kasia Chmielinski (Co-founder, The Data Nutrition Project)

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

LLM workflows for product managers: 3 key takeaways (Niloufar Salehi, Assistant Professor at UC Berkeley) – ProductTank SF

The future of product management: Insights from ProductTank San Francisco

Product lessons learned making early moves with AI in media: Lindsey Jayne (CPO, Financial Times)

A year with ChatGPT and product innovation: Navigating the AI landscape

How to keep your head about generative AI (when everyone is losing theirs) by Claire Woodcock

Using Deep Learning to Find Beautiful Photos by Alex Miller

Emily Tate