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

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