Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute: Learn more about Ray AI Libraries: Data: Scalable Datasets for ML Train: Distributed Training Tune: Scalable Hyperparameter Tuning RLlib: Scalable Reinforcement Learning Serve: Scalable and Programmable Serving Or more about Ray Core an
Welcome to the BID Data Project! Here you will find resources for the fastest Big Data tools on the Web. See our Benchmarks on github. BIDMach running on a single GPU-equipped host holds the records for many common machine learning problems, on single nodes or clusters. Try It! BIDMach is an interactive environment designed to make it extremely easy to build and use machine learning models. BIDMac
Supercharge your deployments with: Production-Ready Inference Servers optimized for popular ML frameworksAdvanced Experimentation and Traffic Splitting including multi-armed bandits, A/B tests, shadows and canariesOptimize Infrastructure Resource Allocation to cost-effectively manage deployed models Gain insights into operational behavior then track, and respond with: Custom Alerts when certain me
The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems efficiently. When the data size and/or the problem dimension is large, a distributed version of ADMM can be used, which is capable of distributing the computation load and the data set to a network of computing nodes. Unfo
Aiming at solving large-scale learning problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the learning problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology. However, traditional synchronized computation do
The growth of data on the web has made it harder to employ many machine learning algorithms on the full data sets. For personalization problems in particular, where data sampling is often not an option, innovating on distributed algorithm design is necessary to allow us to scale to these constantly growing data sets. Collaborative filtering (CF) is one of the important areas where this applies. CF
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