AISE - System Engineering for Artificial Intelligence, Machine Learning, and Big Data

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AISE lets you select or customize your machine learning or big data platform and start working just in minutes.

Company infrastructure   Personal tool
In clouds   On-premises

launch

How it Works

get Get
Select a data science stack from a collection of pre-built stacks or customize a stack to your task choosing languages, frameworks, tools, libraries, and datasets.
deploy Deploy
Launch it to a public cloud from the web panel, install on your server, or download a virtual machine and run on your workstation or private cloud.
use Use
The software is ready for immediate use just after the start. Access to the command line via console or SSH, to the network API, or to the web interface via a browser.

Features

Ready to use
Fully configured and ready-to-use data science platform up and running in minutes with just a few clicks.
Highly customizable
Software stack customization by choosing frameworks, libraries, their forks and versions needed for your task.
Performance optimization
Software modules, optimized and auto-adapting to different hardware configurations (CPU, GPU) to get maximum performance.
Runs everywhere
Programs run the same on any cloud hosting, in a private corporate cloud, on a server, workstation or laptop.
Easy provisioning
Autoinstall scripts into any Linux OS, virtual machines or containers for downloading, web panel for launching to public clouds.
Up-to-date
Carefully maintained, integrated, and tested software packages with new features and bug fixes.

Technologies

AISE leverages the power of all technology out there

TensorFlow Caffe2 Python Julia NVidia CUDA Amazon Web Services OpenStack VMware Docker

…and many more

Stacks

AISE Stacks provides ready-to-use software environments for developing machine learning models and serving them in production. The stacks include the most popular software frameworks and libraries.

Browse through AISE stacks

Pipelines

MindMaster

AISE MindMaster is a machine learning pipeline that enables quickly and easily develop and operate machine learning models. It provides software stacks and management tools for all steps of the pipeline while removing all the barriers that typically slow down the process. The stacks include integrated Jupyter authoring notebook environments, production environments for training models, and serving environments to deploy models in production.

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