Snacks Weekly on Data Science

In this episode, we take a look at the learnings from an online grocery tech company regarding their development of machine learning models for scalable demand forecasting.

Based on the tech blog from Picnic International, the link is provided here for your reference: https://blog.picnic.nl/running-demand-forecasting-machine-learning-models-at-scale-bd058c9d4aa7

In this episode, we discuss the online-offline discrepancy problem in machine learning models and explore Pinterest's journey to tackle this challenge in their ads ranking system.

Based on the tech blog from Pinterest, the link is provided here for your reference: https://medium.com/pinterest-engineering/handling-online-offline-discrepancy-in-pinterest-ads-ranking-system-8fd662da4c2d

In this episode, we explore why and how Instacart’s advertising team unified multiple browse click-through-rate prediction models by leveraging an advanced deep learning architecture.

Based on the tech blog from Instacart, the link is provided here for your reference: https://tech.instacart.com/one-model-to-serve-them-all-0eb6bf60b00d

In this episode, we explore how ZipRecruiter, an online employment marketplace, leverages deep learning and Encoder modules to develop their recommendation system, which is designed for better matching job seekers with job listings.

Based on the tech blog from ZipRecruiter, the link is provided here for your reference: https://medium.com/ziprecruiter-tech/multimodal-learning-for-employment-marketplace-recommendation-ee67bdbede53

In this episode, we look at how Oscar Health, a health insurance company, leverages the large language model (LLM), ChatGPT-4, to develop its claim assistant feature. They have built a reasonable assessment system and deployed several strategies to guide GPT-4 in further improvements.

Based on the tech blog from Oscar Health, the link is provided here for your reference: https://medium.com/oscar-tech/oscar-claim-assistant-built-on-gpt-4-5bd7eb4d6129

This episode shares how the applied science team developed an ad simulation system as a more efficient and economical evaluation method for the Uber Eats Ads business. This tool has proven instrumental in guiding and expediting product development and driving growth.

Based on the tech blog from Uber, the link is provided here for your reference: https://www.uber.com/blog/unleashing-the-power-of-ads-simulation/

This episode shares how the HelloFresh data science team explores the use of generative AI to create new food recipes.

Based on the tech blog shared by Data Scientist from HelloFresh, the blog link is: https://medium.com/hellofresh-dev/recipes-and-generative-ai-6d74a107860c

This podcast episode delves into my journey of utilizing unconventional learning methods to fulfill my 2023 New Year's resolution of enhancing my chess skills. The high-level concept and philosophy apply to Learning in general and it aims to inspire others to reflect on their best learning strategies on various topics, such as Data Science and Machine Learning.

This episode includes a re-distribution of the interview I had with Daliana Liu on her podcast "The Data Scientist Show" (https://open.spotify.com/episode/0HzWSLIWpz6iHJiKXs0GNJ)

There, I shared my thoughts about the focus difference between machine learning engineering and product data science, some projects I worked on at Uber and LinkedIn, as well as other topics: such as why I transitioned from an individual contributor into a manager; and the cultures I experienced in different tech companies.

This episode discusses one company's journey to enable product personalization, starting with customer cohort based recommendation to user-level personalized experience using Learning to Rank algorithm.

Based on the tech blog shared by Data Scientists from CARS24, the blog link is: ⁠⁠https://medium.com/cars24-data-science-blog/personalized-buyer-listings-at-cars24-an-overview-83d8428bd7d9