10 principais feast.dev concorrentes

featurestore.org

The feature store is the central place to store curated features for machine learning pipelines, FSML aims to create content for information and knowledge in the ever evolving feature store's world and surrounding data and AI environment.

kimola.com

Read Kimola’s Privacy Policy to understand data collection, security measures, AI processing, and your rights under our updated policy.

storytell.ai

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

drivendata.org

START HERE! Help public safety agencies share data while protecting privacy. This is part of a series of contests to develop algorithms that preserve the usefulness of temporal map data while guaranteeing individual privacy is protected.

blazesql.com

this ai data analytics chatbot generates sql queries with ai, like chatgpt for your sql database. connect it to your sql database for cutting edge ai data analysis, and let it work for you.

footygpt.com

footyGPT is a cutting-edge AI-powered football assistant that provides real-time, comprehensive information about matches, teams, and players. Our platform offers data-driven predictions for all upcoming games, giving you a competitive edge whether you're a casual fan or a serious bettor

opensurvey.io

See how Starbucks used Dataspace to upgrade in-app surveys, gather real-time feedback, and speed up decisions.

paradigmai.com

Evaluate commercial property listings with automated valuation metrics, market analysis, and risk assessment to accelerate deal sourcing.

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.

waldo.fyi

Waldo is an AI-powered strategy engine for agencies & brands. Get audience insights, industry trends, and competitive intelligence in one powerful platform.