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ai.io

aiScout from ai.io transforms talent discovery with AI-powered scouting, offering unbiased, data-driven insights to identify potential at scale.

impel.ai

Auto industry innovators rely on Impel for advanced digital merchandising. Driving powerful business results through superior digital merchandising solutions.

instadeep.com

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trendtracker.ai

Discover pharma industry trends with AI. Monitor disruption, strengthen resilience, and drive smarter R&D and strategy decisions.

storytell.ai

Storytell.ai makes AI useful & secure for businesses. Turn enterprise data into actionable insights, boost team productivity, and drive smarter decisions.

datapad.io

In this article, you will discover what ecommerce analytics tools do and the 12 best software you can try out to track KPIs and metrics the right way.

feast.dev

A comprehensive guide to feature stores, their role in machine learning operations, and how they solve key challenges in the ML lifecycle.

lightgbm.readthedocs.io

Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, based on Ke, Guolin et al. (2017) . This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, LightGBM has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, LightGBM can achieve a linear speed-up in training time by using multiple machines.