Brightics AI Accelerator
Save time and deliver value faster with AutoML and AutoDL in only a few lines of code.
Progressive enterprise companies are turning to automated machine learning and deep learning to increase productivity up to 250x using distributed compute clusters. AI Accelerator provides the simplest, fastest and easiest Automated Machine Learning (AutoML) and Deep Learning (AutoDL) platform for professional ML/DL scientists who want to reduce model training from weeks to hours with only a few lines of code.
What can Brightics AI Accelerator do for you and your business?
Lowers the fundamental barrier to AI progress by shrinking data preparation, training, and optimization up to 250x while allowing Machine Learning and Deep Learning Scientists to add value without DevOps, IT or distributed clustering expertise.
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 and AutoDL 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, 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.
Discover the capabilities of Brightics AI Accelerator
Take a closer look
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 4 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.
Other ways we can assist you
Whether you're looking for a specific business solution or just need some questions answered, we're here to help.