Snacks Weekly on Data Science

In this episode, we will explore Uber’s Model Excellence Scores (MES) framework, a robust system designed to maintain and enhance the quality of machine learning models at scale. We will unpack its core components—indicators, objectives, and agreements—and explain how they work together to ensure model reliability and performance. This framework enables Uber’s ML ecosystem to operate seamlessly and efficiently, driving both innovation and operational excellence.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/enhancing-the-quality-of-machine-learning-systems-at-scale/ 

In this episode, we’ll explore RazorPay, its business, and the critical role customer service plays in its success. We'll dive into how RazorPay revolutionized customer ticket categorization using generative AI. By replacing customer-selected categories with an AI-driven system, they enabled automatic interpretation of ticket details. This approach incorporated pre-processing, prompt engineering, and a robust knowledge base powered by Retrieval-Augmented Generation (RAG), demonstrating the transformative potential of generative AI in elevating customer service experiences.

For more details, you can refer to their published tech blog, linked here for your reference: https://engineering.razorpay.com/enhancing-customer-support-through-ai-powered-ticket-issue-categorization-a1795d9f0f3c

In this episode, we will explore the foundational concepts of causal analysis, focusing on its two main pillars: causal discovery and causal inference. We will discuss the types of questions these pillars aim to answer and provide illustrations of related methodologies to better clarify their concepts.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/causal-analysis-overview-causal-inference-versus-experimentation-versus-causal-discovery-d7c4ca99e3e4

In this episode, we will explore how Noom developed a customized AI assistant solution using generative AI models. By incorporating key components such as prompt engineering, dynamic personalization, and integration with their knowledge base, the team created a smarter, more reliable assistant to support their customers. This approach offers valuable insights for anyone looking to build tailored generative AI solutions.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/noom-engineering/inside-welli-nooms-new-ai-powered-health-assistant-cf5c050e5ae8

In this episode, we will explore Airbnb’s approach to personal data classification. We will begin by introducing the importance of protecting personal data. Next, we will examine the technical solution, where the three pillars—Catalog, Detection, and Reconciliation—form the backbone of their workflow. Finally, we will discuss how performance metrics are implemented to ensure the system remains reliable and continuously improves.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airbnb-engineering/personal-data-classification-2d816d8ea516

In this New Year’s episode, I reflect on one of my earliest data science projects. My son will “interview” me about my experiences during that time. The key project discussed in this episode is also featured in a LinkedIn article I wrote a few years ago (⁠https://www.linkedin.com/pulse/my-first-data-science-project-pan-wu/⁠⁠).

I’d love for you to check it out, leave a 5-star review, and subscribe to the channel! Thank you for your support, and I wish you all a fulfilling 2024 and a very Happy New Year in 2025!

In this episode, we will explore Netflix’s approach to content recommendation using contextual bandits and reward engineering. We will also discuss the important role of proxy reward functions and how Netflix leverages offline machine learning models to predict delayed customer feedback, enabling them to continuously improve their recommendation engine and deliver a more personalized viewing experience.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/recommending-for-long-term-member-satisfaction-at-netflix-ac15cada49ef

In this episode, we will introduce the concept of paid marketing. We’ll explore how Instacart’s team developed an adaptive experimentation framework that continuously balances exploration and exploitation, ultimately maximizing marketing efficiency.

For more details, you can refer to their published tech blog, linked here for your reference:  https://tech.instacart.com/bandits-for-marketing-optimization-f5a63b9bfaa7

In this episode, we will introduce what CVS Health is and the importance of product recommendations for their business needs. We will delve into how their data science team leveraged advanced technologies, including Graph Neural Networks and generative AI models like GPT-4, to develop a prototype system to make product bundle recommendations.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/cvs-health-tech-blog/ai-in-cvs-front-store-e-commerce-building-a-complementary-product-bundle-recommender-dc6c71dee682

In this episode, we will introduce the issue of data imbalance and its impact on machine learning models, especially for text data. We will discuss a range of augmentation techniques and walked through how Walmart’s data science team built an automated augmentation module to apply these techniques consistently and effectively.

For more details, you can refer to their published tech blog, linked here for your reference:  https://medium.com/walmartglobaltech/augmentation-techniques-for-imbalanced-text-classification-f0d29c0f8ce1