Snacks Weekly on Data Science

By Pan WuEducation

Snacks Weekly on Data Science episodes

July 08, 2024 Optimize Feature Selection with Generic Algorithm [JustEatTakeaway.com/Grubhub]
In this episode, we will discuss what is feature selection and how JustEatTakeaway.com leverages generic algorithms as one practice to to optimize their feature selection.
For more details, you can refer to their published tech blog, linked here for your reference:
https://medium.com/justeattakeaway-tech/optimising-feature-selection-with-genetic-algorithms-an-easy-to-use-python-script-dde44cc9c053
15 min
July 01, 2024 Monitoring Mechanisms for Recommendation Systems [Tubi TV]
In this episode, we will discuss how to build an effective monitoring mechanism for a recommendation system. We will cover the basic components of various recommendation processes and explore how the Tubi TV Engineering team developed their monitoring flow.
For more details, you can refer to their published tech blog, linked here for your reference: https://code.tubitv.com/how-to-monitor-a-recommender-system-6d720c922c90

13 min
June 24, 2024 Determine Causal Effects through Adoptor Analysis [Walmart]
In this episode, we will introduce the concept of causal inference, and discuss how the data scientist team from Walmart determines Causal Effects when A/B Tests are Infeasible through Adopter Analysis.
Based on their published tech blog, with the link provided here for your reference: https://medium.com/walmartglobaltech/how-to-determine-causal-effects-when-a-b-tests-are-infeasible-through-adopter-analysis-b06f2d51a633
14 min
June 17, 2024 Measure Web Performance with Composite Metric [Indeed]
In this episode, we will discuss the importance of metrics for the business. We will share the journey of how Indeed started with a single metric to measure client-side performance, and eventually converged into a composite metric serving as a comprehensive measure
Based on their published tech blog, with the link provided here for your reference:
https://engineering.indeedblog.com/blog/2024/01/composite-web-performance-metric
17 min
June 10, 2024 Developing Text-to-SQL Feature with Large Language Models (LLMs) [Pinterest]
In this episode, we will explore how Pinterest uses generative AI technology to develop a text-to-SQL feature for their data analytics team. We will examine the general architecture of their two iterations, regarding how the team has enhanced the AI product to support better a wider range of analytics use cases with improved productivity.
Based on their published tech blog, with the link provided here for your reference: https://medium.com/pinterest-engineering/how-we-built-text-to-sql-at-pinterest-30bad30dabff
13 min
June 03, 2024 A/B Testing with Cluster Experimentation Under Strong Network Effects [Meta]
In this episode, we'll discuss what network effects are, how they introduce challenges in the standard A/B testing framework, and how the cluster experimentation method can be leveraged to address these challenges. We will also delve into the technical details of how clusters can be generated, and evaluated, and the associated trade-offs that need to be considered.
Based on their published tech blog, with the link provided here for your reference: https://medium.com/@AnalyticsAtMeta/how-meta-tests-products-with-strong-network-effects-96003a056c2c
16 min
May 27, 2024 Measuring Marketing Effectiveness with Geo-experimentation [Grammarly]
In this episode, we'll explore how the data science team from Grammarly developed their geo-experimentation to measure marketing effectiveness. We will cover about three components in designing an A/B testing experiment, as well as considerations regarding the opportunistic costs of the experimentation.
Based on their published tech blog, with the link provided here for your reference: https://www.grammarly.com/blog/engineering/measuring-marketing-effectiveness-in-a-cookie-less-world/
15 min
May 20, 2024 Monte Carlo Simulatoin for Sampled Success Metrics [Shopify]
In this episode, we'll explore how the data science team from Shopify leverages Monte Carlo Simulation to develop their sampled success metrics. We'll discuss what is sampled success metrics, the associated trade-offs needed to build them, and how Monte Carlo simulation can be used to inform decisions.

Based on their published tech blog, with the link provided here for your reference: https://shopify.engineering/monte-carlo-simulations-sampled-success-metrics
16 min
May 13, 2024 Building Generative AI Product for Customer Segmentation [Klaviyo]
In this episode, we'll explore how the data science team from Klaviyo developed a Generative AI Product that enhances experiences and enables efficient customer segmentation. We'll also discuss two key concepts in Generative AI: prompt chaining and few-shot learning.

Based on their published tech blog, with the link provided here for your reference: https://klaviyo.tech/building-segments-ai-fe33d9cab822
16 min
May 06, 2024 Machine Learning Solution for Failed Job Auto Remediation [Netflix]
Description: In this episode, we will talk about the importance of remediating failed workflow jobs to reduce business infrastructure costs. We delve into Netflix's approach, which involves enhancing their existing rule-based error classifier with advanced machine learning models. This allowed for auto-remediation, improving the handling of memory configuration and unclassified errors, ultimately leading to substantial cost savings.

Based on their published tech blog, with the link provided here for your reference: https://netflixtechblog.com/evolving-from-rule-based-classifier-machine-learning-powered-auto-remediation-in-netflix-data-039d5efd115b
14 min

About Snacks Weekly on Data Science

From the publisher's feed

This podcast is about making data science and machine learning knowledge accessible and less intimidating. Every week, I will handpick one selected industrial tech blog to break it down. We will discuss some key data science concepts and machine learning algorithms, and how they are applied in those real-world applications.