Snacks Weekly on Data Science

In this episode, we explore how Agoda used large language models (LLMs) to improve user experience through building a conversational AI product. By focusing on prompt engineering, grounding data, and smart evaluation, the team built a scalable assistant that adds real value to the user journey.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/agoda-engineering/how-we-built-agodas-property-ama-bot-to-simplify-travel-decisions-b861c7ec7ff1

In this episode, we explore how Meta tackles the complex challenge of setting aligned, measurable, and high-impact goals across a vast organization. Whether you’re in data science, analytics, or product leadership, this episode offers practical insights into building a more effective goal-setting system.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/how-facebook-sets-goals-94cee1c7f44f

In this episode, we will explore how Hike applied transformer-based models to predict user behavior in their Rush Gaming Universe. We will look at the business motivation and break down the technical solution, from input features to prediction and evaluation. This case is a good example of how modern deep learning techniques can drive real impact in improving user experience.
For more details, you can refer to their published tech blog, linked here for your reference: https://blog.hike.in/predicting-user-actions-with-transformer-based-models-ffd1c6b68e99

In this episode, we will explore quantization techniques for language models. We will look at the business motivation—making large language models more efficient—and unpack the technical solutions that make this possible. 

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@EsperantoTech/quantization-and-mixed-mode-techniques-for-small-language-models-b3366dbad554

In this episode, we’ll explore the unique security challenges posed by agentic AI systems and why embedding trust and safety into these systems from the ground up is critical. We’ll review a few key ingredients for building a secure agentic AI future.

For more details, you can refer to the blog, linked here for your reference: https://medium.com/intuit-engineering/owasp-dishes-out-key-ingredients-for-a-secure-agentic-ai-future-be862e167d6c

In this episode, we explore how Oda scaled its A/B testing practices alongside its business growth, focusing not only on building a technical platform but also on creating a culture that supports high-quality, reliable experimentation.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/oda-product-tech/odas-online-experimentation-journey-lessons-learned-and-best-practices-7091c318beeb

In this episode, we explore how Booking.com tackled the challenge of predicting reservation cancellations in an ever-changing travel landscape. By shifting from a traditional classification model to a survival modeling approach, the team developed more time-sensitive and flexible predictions that better support their business needs and decision-making.

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 episode, we will explore how Workday tackle the challenge of measuring the cost of GenAI features. We looked at why LLM-powered features require a new approach to cost tracking, and how the team engineered a telemetry-driven system to make those costs visible, actionable, and fair.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/workday-engineering/measuring-the-unit-cost-of-genai-features-370f090c3982 

In this episode, we will discuss how Expedia’s recommendation system is designed to handle both standard destination searches and property-specific searches. While traditional ranking models optimize for broad search behavior, Expedia’s team refines their learning-to-rank approach by integrating property similarity, ensuring travelers get recommendations that align with their intent.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/learning-to-rank-at-expedia-group-how-to-adapt-the-property-search-result-page-based-on-f4ebef78c94b 

In this episode, we will explore why evaluating LLM-based chatbots is critical for businesses, the limitations of traditional evaluation methods, and what could be a good robust evaluation framework covering both search performance and LLM-specific metrics. 
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/evaluating-llm-based-chatbots-a-comprehensive-guide-to-performance-metrics-9c2388556d3e