In this episode, we will explore Urban Company’s business needs and the role of causal machine learning in addressing them. We will delve into three key models—S-learner, T-learner, and X-learner—and used a simple example to illustrate how each one works. These models provide valuable insights by offering more accurate estimates of cause-and-effect relationships, helping companies make better data-driven decisions.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/uc-engineering/how-urban-company-leverages-causal-inference-to-power-data-driven-decisions-a339cfcaa0e2

In this episode, we will explore how Faire tackled the challenge of product categorization. They initially used the K-nearest neighbor algorithm with CLIP embeddings, which improved categorization but still required manual corrections. To further enhance accuracy, the team fine-tuned a vision-language model using their in-house dataset, increasing accuracy significantly. This solution showcases how advanced machine learning can drive business efficiency.

For more details, you can refer to their published tech blog, linked here for your reference: https://craft.faire.com/advancing-product-categorization-with-vision-language-models-the-power-of-fine-tuned-llava-2f4bf024a102

In this episode, we will discuss why Walmart relies on online experimentation to drive data-driven decisions and how reducing variance is a crucial challenge to making these experiments more efficient. We will also introduce the CUPED methodology, explaining how Walmart leverages it to speed up its experimentation process, enabling faster, more reliable insights for continuous improvement.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/walmartglobaltech/how-walmart-leverages-cuped-and-reduces-experimentation-lifecycle-eae2446c2ee4 

In this episode, we will explore the challenge Netflix faces in building machine learning models for video understanding. We will examine Netflix’s solution—a self-service system with active learning that empowers video experts to participate in creating and refining machine learning classifiers through a streamlined, three-step process.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://netflixtechblog.com/video-annotator-building-video-classifiers-using-vision-language-models-and-active-learning-8ebdda0b2db4

In this episode, we explore the concept of incrementality in marketing, and how the data science team at Expedia leverages geo-testing to successfully measure their marketing campaign’s  incrementality 

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/measuring-marketing-success-the-power-of-incrementality-and-geo-testing-1acd4291545d

In this episode, we explore the concept of out-of-app marketing and its importance for businesses like Uber. We'll discuss the challenges involved in personalizing out-of-app marketing messages, the essential components of their recommendation architecture, and how the team created customized solutions to improve personalization and relevance in their recommendations.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/personalized-marketing-at-scale/

In this episode, we will discuss the importance of ETA for ridesharing apps and the challenges of providing a reliable ETA to users upfront. We delved into the practices of the machine learning team at Lyft, examining how they developed a solution to address this unique challenge using a lightweight machine learning model.

For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/eta-estimated-time-of-arrival-reliability-at-lyft-d4ca2720bda8

In this episode, we will explore how Yelp navigates the challenges of incorporating video reviews into its platform. We will discuss the use of machine learning to detect inappropriate content and the strategies to maintain the quality and integrity of its platform.

For more details, you can refer to their published tech blog, linked here for your reference:
https://engineeringblog.yelp.com/2024/03/moderating-inappropriate-video-content-at-yelp.html

In this episode, we will introduce what a brand is and why measuring brand perception is crucial. We will discuss how social media data can be used to achieve this and look at how Airbnb leverages deep learning methods and word embedding technologies to quantify brand perception more effectively.

For more details, you can refer to their published tech blog, linked here for your reference:
https://medium.com/airbnb-engineering/airbnb-brandometer-powering-brand-perception-measurement-on-social-media-data-with-ai-c83019408051

In this episode, we will explore the concept of quasi-experimentation and its role in Instacart's decision-making process. We will take a closer look at one specific methodology, regression discontinuity design, explaining its key concepts and demonstrating how it works through an interesting example.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/optimizing-at-the-edge-using-regression-discontinuity-designs-to-power-decision-making-51e296615046