Snacks Weekly on Data Science

Product classification evolution [Shopify]

In this episode, we explore how Shopify evolved its product classification system across three major stages: from a traditional logistic regression model with TF-IDF features, to a multi-modal approach combining text and images, and finally to Vision Language Models built on top of a standardized and evolving product taxonomy. We also look at how architectural design and inference optimization are just as important as model accuracy in real-world machine learning systems.For more details, you can refer to their published tech blog, linked here for your reference: https://shopify.engineering/evolution-product-classification

Building an Ads System from Scratch [Faire]

In this episode, we explore how Faire built its ads system from scratch. On the business side, we discuss why ads matter for a growing marketplace: enabling brand discovery, creating a new revenue stream, and strengthening the overall ecosystem. On the technical side, we break down the three core components—Ads Delivery, Ads Manager, and Ads Foundation—and examine key considerations such as optimizing for long-term brand–retailer relationships and shipping a complex system within just six months.For more details, you can refer to their published tech blog, which is linked in the episode description: https://craft.faire.com/building-faires-ads-system-from-scratch-5c24fc916995

Optimize SQL Stored Procedures with LLM [Agoda]

In this episode, we explore how Agoda tackled a costly engineering bottleneck by integrating GPT into their CI/CD pipeline to analyze failing SQL stored procedures automatically and suggest optimizations — complete with rewritten queries, index recommendations, and side-by-side performance comparisons. The result is a human-in-the-loop system where AI handles the heavy lifting and engineers make the final call, leading to significant improvements in engineering productivity.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/agoda-engineering/how-agoda-uses-gpt-to-optimize-sql-stored-procedures-in-ci-cd-29caf730c46c

LLM-Empowered Job Search [LinkedIn]

In this episode, we explore how LinkedIn is reimagining job search with AI and large language models — evolving from rigid, keyword-based systems to flexible, intent-aware experiences that feel more conversational and personalized.For more details, you can refer to their published tech blog, linked here for your reference: https://www.linkedin.com/blog/engineering/ai/building-the-next-generation-of-job-search-at-linkedin

Personalized CRM with Bandit algorithm [Uber]

In this episode, we explore how Uber tackled the challenge of personalizing CRM communications at scale through contextual bandit strategies enhanced with generative AI embeddings, lightweight and powerful models like LinUCB and XGBoost, and smart decision augmentation with SquareCB. This work shows how data science can take a core business need—delivering relevant user communications—and build systems that adapt in near real time to maximize impact.For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/enhancing-personalized-crm