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

In this episode, we explore the importance of covariate selection in causal inference and how different types of variables can influence the results. The discussion highlights why careful covariate selection is essential for generating reliable insights and enabling smarter, evidence-based business decisions.
For more details, you can refer to their published tech blog, linked here for your reference: https://booking.ai/covariate-selection-in-causal-inference-good-and-bad-controls-5f56126a984a

In this episode, we explore how Klaviyo used counterfactual learning and uplift modeling to move beyond the question of which treatment works — to the deeper question of for whom it works. We’ll see how the team combined randomized experiments, causal inference techniques, and uplift modeling to power a product that helps marketers deliver smarter, more personalized messages.
For more details, you can refer to their published tech blog, linked here for your reference: https://klaviyo.tech/the-stats-that-tell-you-what-could-have-been-counterfactual-learning-and-uplift-modeling-e95d3b712d8a

In this Halloween special episode, we explore some fun facts and surprising data behind these festive favorites: Did you know Illinois is the top pumpkin-producing state, harvesting nearly 40% of all pumpkins in the U.S.? Or that Reese’s Peanut Butter Cups consistently rank as America’s most popular Halloween candy? And that over — or at least — 20% of pet owners now dress up their pets for Halloween? Now, let’s dive into these facts and the history behind the holiday. Enjoy!

In this episode, we explore how Whatnot improved its feed ranking system by moving from batch predictions to online inference—enabling the platform to scale effectively while capturing real-time marketplace dynamics. This evolution reflects a broader shift in recommendation systems toward more adaptive, real-time personalization.

For more details, check out the full tech blog from the Whatnot engineering team: https://medium.com/whatnot-engineering/evolving-feed-ranking-at-whatnot-25adb116aeb6

In this episode, we explore Tripadvisor’s self-serve experimentation platform for marketing. On the business side, the challenge was measuring campaign effectiveness in a messy, external environment where clean randomization isn’t always possible. On the technical side, the TripAdvisor team developed a system that applies causal inference techniques—particularly the difference-in-differences method—to deliver reliable estimates of campaign impact.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/tripadvisor/introducing-baldur-tripadvisors-self-serve-experimentation-tool-for-marketing-7fc9933b25cc

In this episode, we look at how Meta addressed the challenge of feature selection at scale through Global Feature Importance—a system that aggregates insights across models to surface the most valuable features. This approach not only streamlines model development but also enables machine learning engineers to iterate more effectively and build models that deliver stronger business impact.
For more details, check out Meta’s published tech blog here: https://medium.com/@AnalyticsAtMeta/collective-wisdom-of-models-advanced-feature-importance-techniques-at-meta-1a7a8d2f9e27

In this episode, we explore how to evaluate the retrieval-augmented generation (RAG) capabilities of small language models. On the business side, we discuss why RAG, long context windows, and small language models are critical for building scalable and reliable AI systems. On the technical side, we walk through the Needle-in-a-Haystack methodology and discuss key findings about retrieval performance across different models.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/evaluating-rag-capabilities-of-small-language-models-e7531b3a5061

In this episode, we explore how Netflix enhanced recommendation personalization using foundation models. These models can process massive user histories through tokenization and attention mechanisms, while also addressing the cold-start problem with hybrid embeddings. The work highlights how principles from large language models can be adapted to build more effective recommendation systems at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39

In this episode, we explore how Vanguard evaluated standard A/B testing against multi-armed bandits for digital experimentation. Their simulated study showed that A/B testing is often the better choice when dealing with a small number of variations, while bandit strategies, such as Thompson Sampling, become more effective as the number of variations increases. The broader lesson is that experimentation design should always be context-aware—balancing simplicity, speed, and interpretability based on your business needs.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/vanguard-technology/smarter-web-wins-a-b-testing-vs-multi-armed-bandits-unpacked-7f5032358513

In this episode, we explore how Instacart tackled the challenge of extracting accurate product attributes at scale. We discuss different solutions—starting with SQL rules, moving to text-based ML models, and finally, Instacart’s multi-modal LLM platform, PARSE. By blending text and image data and enabling rapid configuration, PARSE demonstrates how modern AI tools can streamline data pipelines, reduce engineering overhead, and deliver better user experiences.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/multi-modal-catalog-attribute-extraction-platform-at-instacart-b9228754a527

In this episode, we explore how Airbnb developed a structured framework that combines unsupervised clustering and supervised modeling to classify listings into meaningful supply personas based on availability patterns. This data-driven approach helps Airbnb enhance personalization, improve experimentation, and gain deeper insights into its global supply base.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airbnb-engineering/from-data-to-insights-segmenting-airbnbs-supply-c88aa2bb9399