In this episode, we will talk about what is position bias in recommendation systems, and how the applied scientist team at Uber tackled this challenge.

Based on their published tech blog, with the link provided here for your reference: https://www.uber.com/blog/improving-uber-eats-home-feed-recommendations

In this episode, we will take a look at one analytical approach in decision-making called Analytical Hierarchy Processing (AHP). The New York Times team leverages this AHP process to help with making better collective decisions in their technology choices for privacy.

Based on their published tech blog, with the link provided here for your reference: https://open.nytimes.com/collective-decision-making-with-ahp-3ef819e5bc2a

In this episode, we delve into a study conducted by Intuit on measuring the productivity impact of the Gen AI tool on their data analyst team. It demonstrates exciting positive productivity gains, offering an exciting outlook for many in the industry.

Based on their published tech blog, with the link provided here for your reference: 

​​https://medium.com/intuit-engineering/how-intuit-data-analysts-write-sql-2x-faster-with-internal-genai-tool-c3b9d482208a 

Description: In this episode, we discuss how the data science team at Microsoft designed perturbation analysis to understand the Large Language Model’s performance on commonly seen tasks.

Based on their published tech blog, with the link provided here for your reference: 

https://medium.com/data-science-at-microsoft/perturbation-analysis-and-llms-how-sensitive-are-llms-to-their-input-91a8407a971f

In this episode, we discuss how DraftKings leverages Monte Carlo simulation to predict tennis game outcomes and generate probabilities to power its online gambling service.

Based on their published tech blog, with the link provided here for your reference: 

https://medium.com/draftkings-engineering/building-a-tennis-simulation-d6afdaa97d19 

In this episode, we discuss the insights shared by Expedia's Machine Learning Engineering team on how they leverage the two-tower neural network architecture in the candidate generation stage of their recommendation system.

Based on their published tech blog, with the link provided here for your reference: https://medium.com/expedia-group-tech/candidate-generation-using-a-two-tower-approach-with-expedia-group-traveler-data-ca6a0dcab83e

In this episode, we discuss how the Engineering team at Walmart created a customized Large Language Model (LLM) agent with Retrieval Augmented Generation (RAG) Technology to improve their agile planning efficiency.

Based on their published tech blog, with the link provided here for your reference: https://medium.com/walmartglobaltech/an-autonomous-agent-for-agile-planning-98303e194e08

In this episode, we discuss why and how Intuit leverages automated sanity checks to streamline its machine learning development process.

Based on their published tech blog, with the link provided here for your reference: https://medium.com/intuit-engineering/how-to-streamline-ml-model-deployment-automated-sanity-checks-64a23166fdc5

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