1.Recommending for Long-Term Member Satisfaction at Netflix
How Netflix reinvents content recommendations for long-term satisfaction?
In this article, how Netflix's recommendation system goes beyond short-term clicks to focus on long-term satisfaction. Instead of just promoting content to get an immediate response, Netflix looks at factors that keep users engaged and coming back for more—using sophisticated machine learning techniques to predict what you'll love in the long run.
https://netflixtechblog.com/recommending-for-long-term-member-satisfaction-at-netflix-ac15cada49ef
2.How Meta animates AI-generated images at scale
How does meta handle the challenge of scaling AI image animations for billions of users?
In this blog, the focus is on how Meta overcame the challenges of scaling AI-generated image animations. The post dives into techniques like latency reduction and GPU efficiency, allowing Meta to deliver animations quickly and efficiently to a global audience.
3.Schema in Data Warehouse: A Comprehensive Guide?
How schemas shape data warehousing efficiency?
This article explains the critical role schemas play in structuring data, ensuring efficient storage, and optimising query performance. It breaks down common schema types—like star, snowflake, and fact constellation—highlighting their impact on reducing redundancy and improving data retrieval.
4.Making Uber’s ExperimentEvaluation Engine 100x Faster
How did Uber make experiment evaluations 100x faster?
This article explores how Uber redesigned its experimentation platform by moving from remote to local evaluations. By eliminating the need for network calls, Uber achieved remarkable speed improvements, reducing latency and enhancing the reliability of its services across all business units.
https://www.uber.com/en-IN/blog/making-ubers-experiment-evaluation-engine-100x-faster/
5.How HomeToGo improved our Superset Monitoring Framework
How can data dashboards be made more efficient and actionable?
This article explains how HomeToGo improved their Superset monitoring framework by ingesting metadata into a data warehouse and redefining usage metrics. These improvements allowed them to identify and decommission under-utilised dashboards, reducing clutter and focusing on what truly drives business insights.
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