Turnkey AI project delivery from initial assessment to actionable intelligence solution deployment for your specific use case and desired results.
Lowers the fundamental barrier to AI progress by shrinking data preparation, training, and optimization time from months to hours while allowing Machine Learning and Deep Learning Scientists to concentrate on devising algorithms, optimizing them and measuring how they perform rather than building and maintaining infrastructure.
Lift and shift desktop AI model training to automated, distributed training in order to scale image throughput near linearly with each additional GPU server up to 256 via high speed interconnect. The result of increasing image throughput nearly linearly is quick and efficient model convergence to state-of-the-art accuracy in supercomputer speed.
Automated Machine Learning consolidates a month of work into 10 minutes. Automated Deep Learning consolidates 3 weeks of work into 1 hour. Automatic provisioning and orchestration of jobs and resource clean-ups when completed saves you money.
Brightics AI Accelerator is well suited to deliver AutoML solutions fully on-premises as well as in your private cloud environment. Your proprietary data and model train inside your secure data center or protected network.
From a single user interface of the Jupyter Notebook or PyCharm IDE, orchestrate large job setup, data preparation, training, inference and tear-down without any specialized knowledge of DevOps or IT clustering with just one Python API call.
Assessment service explores your specific use case and determines a scope of work to produce desired results while a Data Assessment determines the predictive power of your data based on a snapshot of the data over a milestone-based, phased approach and make a GO/NO-GO decision to proceed.
Automates and accelerates model training on tabular data by using automated model selection from scikit-learn, automated feature synthesis, and hyper-parameter search optimization. AutoML with synthetic feature generation exploits up to 256 CPU cores simultaneously to produce a scikit-learn model in 1 hour versus 2 months using traditional methods.
Automates and accelerates deep learning model training using data-parallel, distributed synchronous Horovod Ring-All-Reduce TensorFlow and PyTorch frameworks with minimal code. AutoDL exploits up to 256 GPUs per iteration to produce a model in 2 hours versus 3 weeks using traditional methods. Automates transfer learning for image data considering all models in the model zoo with hyper-parameter search.
Increases image data throughput near linearly with large numbers of up to 256 GPUs in your cluster for Keras, TensorFlow and PyTorch training
Integrated environment for Data Science and Machine Learning teams to collaborate using simple, automated distributed training, data preparation and inference on large clusters
Data science teams run data preparation, training, and inference jobs entirely from one single interface with minimal code
AI Machine Learning teams run data preparation, training, and inference jobs entirely from PyCharm IDE using REST APIs
Offers simplified one-click installation in the Cloud and accelerated setup for on-premise.
Predicting customer preferences using market data analysis.