In this episode, we will explore the importance of the Large Language Model (LLM) and the forces shaping the LLM economy: competition among AI giants, GPU scarcity, and tokens as the new currency. These dynamics drive innovation and challenge businesses to optimize resources and costs strategically.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/wix-engineering/the-emerging-economy-of-llms-883f2ab13067

In this episode, we will explore why Klaviyo developed its global holdout group feature and how its engineering team overcame the technical challenges. This feature helps Klaviyo’s customers run fair and unbiased experiments across multiple marketing channels, ultimately enhancing the accuracy of their marketing performance insights.

For more details, you can refer to their published tech blog, linked here for your reference: https://klaviyo.tech/creating-global-holdout-groups-8d48c8cf7266

In this episode, we will explore why code reviews are critical for a fast-growing marketplace like Faire and the challenges that come with scaling them manually as the engineering team expands. We’ll dive into how Large Language Models (LLMs) offer a game-changing solution—automating code reviews by providing instant, context-aware feedback, enforcing coding best practices, and integrating seamlessly into existing development workflows.

For more details, you can refer to their published tech blog, linked here for your reference: https://craft.faire.com/automated-code-reviews-with-llms-cf2cc51bb6d3 

In this episode, we will explore how Instacart uses data science to optimize its incentive promotions. We will discuss the business challenge, introduce the concept of surrogate indices, and walk through the step-by-step process of building and applying one.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/instacarts-economics-team-using-surrogate-indices-to-estimate-long-run-heterogeneous-treatment-0bf7bc96c6e6

In this episode, we will explore Meta’s AI scaling challenges and how the company leverages productivity and efficiency to optimize its computing power. We also discuss how analytics insights help identify active levers to improve AI development.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/innovation-demands-compute-how-to-enable-ml-productivity-and-efficiency-38f56cf520f7

In this episode, we will explore how Coupang integrates Large Language Models (LLMs) to enhance its machine learning ecosystem. We'll break down Coupang’s business model, key machine learning categories, and the role of Foundation Models in improving efficiency and accuracy. Additionally, we'll walk through the LLM development lifecycle, discuss critical infrastructure decisions, and examine strategies for overcoming challenges such as GPU scarcity and scaling AI operations.

For more details, you can refer to their published tech blog, linked here for your reference:https://medium.com/coupang-engineering/accelerating-coupangs-ai-journey-with-llms-2817d55004d3 

In this episode, we will explore how Expedia ranks lodging options to optimize both customer experience and business objectives. We will discuss the business problem—how ranking impacts Expedia’s success and the challenges of hotel recommendations. Then, we will break down the data science solution, covering the objective functions, features/signals, machine learning architectures, and the evaluation of the system.
For more details, you can refer to their published tech blog, linked here for your reference:https://medium.com/expedia-group-tech/choosing-the-right-candidates-for-lodging-ranking-d0841bf40c0e

In this episode, we will explore Thumbtack’s business model and the importance of recommendations in helping professionals grow their businesses. We will share the team’s machine learning solution architecture, which involved building two sub-models and deploying them using offline inference to meet the business needs cost-efficiently. This quick and nimble approach to machine learning demonstrates how technology can effectively solve real-world business challenges.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/thumbtack-engineering/how-we-personalized-recommendations-for-professionals-on-thumbtack-40131b57c3a9

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