Snacks Weekly on Data Science

In this episode, we explore how Netflix tackles the challenge of predicting user session intent by extending the capabilities of its foundation model with a hierarchical multi-task learning architecture. This approach helps Netflix better understand what users want in the moment and personalize the experience in real time, ultimately improving its recommendation system at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/fm-intent-predicting-user-session-intent-with-hierarchical-multi-task-learning-94c75e18f4b8

In this episode, we explore how CVS Health builds its product recommendation system to deliver relevant, timely suggestions across millions of customers and thousands of products. We look at the business motivation behind personalization at CVS, and then walk through how the team uses Word2Vec, Euclidean distance, LLM-generated product summaries, and iterative refinement to improve the system step by step.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/cvs-health-tech-blog/enhancing-you-may-also-like-ymal-systems-using-llms-and-word2vec-0340280019d2

In this episode, we explore how Airtable built AI Agents—a system that lets users automate workflows using natural language. We examine the business motivation behind making automation more accessible and break down the technical architecture that ensures these agents are safe, reliable, and tightly integrated into Airtable’s platform.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airtable-eng/how-we-built-ai-agents-at-airtable-70838d73cc43

In this episode, I share a few key observations and reflections drawn from the tech blogs I read throughout 2025. The themes include the rise of real-world LLM applications, a move toward deeply customized machine learning solutions, and the evolving skill sets in data and AI, with continuous learning becoming more important than ever.

I’d also like to express my sincere appreciation to everyone who has listened, read, engaged with, or shared my posts and podcasts this year. Thank you for making this journey so rewarding and fun. I wish you a restful holiday season and an inspiring start to the new year.

In this episode, we explore how Lyft identified the right algorithmic approach for building a real-time spatial-temporal forecasting system. The team evaluated two major model families for this task: classical time-series models and deep neural networks. This study highlights the balance between accuracy and practicality—and serves as a valuable guide for choosing machine learning solutions that truly meet business needs.

For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/real-time-spatial-temporal-forecasting-lyft-fa90b3f3ec24

In this episode, we explore how Uber tackled the challenge of processing an enormous volume of invoices that vary widely in layout, language, and quality. We break down how generative AI plays a central role in helping them build a more flexible and scalable document-processing system. By combining OCR, LLM-based extraction, and a thoughtful human-in-the-loop workflow, Uber created a platform that’s faster, more accurate, and far easier to maintain than traditional rule-based automation.
For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/advancing-invoice-document-processing-using-genai

In this episode, we will explore how Walmart's Engineering team tackled the challenge of optimizing web performance at scale: they set top-line targets, moved from server-centric metrics to user-centric ones like Core Web Vitals, integrated these measures into their experimentation framework, and ultimately drove measurable business impact through improved engagement and organic traffic. 

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/walmartglobaltech/walmart-journey-to-optimize-web-performance-and-drive-business-growth-c3bec8d7780b

In this episode, we explore how Pinterest tackled one of the toughest challenges in large-scale analytics — understanding why metrics move. We discuss how their engineering team built a root cause analysis platform that combines Slice and Dice, General Similarity, and Experiment Effects, with each component addressing a different part of the problem. This system brings together analytics, statistics, and engineering into an actionable workflow, empowering teams to respond faster and with greater confidence.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/pinterest-engineering/the-quest-to-understand-metric-movements-8ab12ae97cda

In this episode, we explore how Airbnb improved search ranking for its map interface — a challenge that sits at the intersection of user behavior, design, and data science. From assuming uniform attention to modeling tiered and spatial attention, Airbnb’s team systematically refined how users interact with map results. This work shows how aligning user attention with booking likelihood can drive real business impact — improving bookings, enhancing customer satisfaction, and increasing overall platform efficiency.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airbnb-engineering/improving-search-ranking-for-maps-13b03f2c2cca

In this episode, we explore how Instacart leverages machine learning to suggest smart replacements for out-of-stock products — a challenge that’s central to the grocery delivery experience. We dive into Instacart’s two-model approach, where a deep learning model uncovers general product relationships across the catalog, and an engagement model learns from customer behavior to personalize those recommendations. Together, they power a system that makes replacements more accurate, relevant, and efficient at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/how-instacart-uses-machine-learning-to-suggest-replacements-for-out-of-stock-products-8f80d03bb5af