Discover how Taboola addresses training-serving feature discrepancies in machine learning models. Learn about Sherlock, our system for identifying and fixing inconsistencies to enhance recommendation accuracy.
Taboola’s Tracks methodology empowers leaders with strategic resource management and cross-functional collaboration to drive innovation and measurable impact.
Discover how Taboola’s Tracks methodology fosters cross-functional collaboration, innovation, and business impact, as we celebrate the success of Maximize Conversions and look ahead.
Explore how Taboola solved a complex memcached cluster network saturation issue impacting recommendation requests, using observability and cache distribution analysis.
Taboola’s unified ranking strategy optimizes ad space allocation, balancing revenue and engagement for publishers by ranking ads and organic content together for a more tailored user experience.
Exploring strategies to improve performance in a diverse infrastructure, addressing issues with Skylake CPUs, AVX512 throttling, and TensorFlow updates, leading to notable enhancements in CPU frequency, load averages, and inference requests per core.
Unlock the secrets of multi-library projects in Android development. From Git submodules to Maven local repositories, discover efficient strategies for organizing and managing dependencies. Learn how to seamlessly work both locally and remotely, ensuring a smooth development cycle.
Learn how meaningful publisher features revolutionize recommendations. Explore data analysis, visualization, and CTR prediction. Witness improved models in action. Say hello to personalized content experiences!
The successful integration of LLMs has expanded news coverage on local publishers’ websites, akin to editors’ broader context, potentially enhancing coverage outcomes, albeit with considerations for latency and cost implications.
How Taboola took our CDC infrastructure to the next level.
