FACULTY in Artificial Intelligence Platform

FACULTY specializes in data exploration, data science, advanced analytics, predictive analytics, and data engineering that empowers organizations to become more data-driven with first-class software, skills, and advisory solutions.

Demo

Users can refer to the company website for a demo https://faculty.ai/request-a-demo/

Pricing

Detailed pricing for this company has not been disclosed, but it is in line with leading competitors in the market. Most software companies and vendors require you to contact them with details so they can offer competitive personalized pricing based on your needs. For the best pricing plans, contact the vendor. 

Features

Create and deploy AI safely - Faculty Platform is designed to build and deploy machine learning and predictive analytics models. It is engineered by Faculty’s data scientists to accelerate iterative data science workflow. Faculty Platform brings essential functionality with the best in open-source tooling.

User controls and cloud server options - Faculty Artificial platform scales data storage and compute resources up and down.

Share, spread and integrate with Faculty Platform - Faculty has developed the platform using open-source technology. It’s scaled with containers and is regularly tested and audited by a CREST-accredited partner. Faculty Platform can connect to any data source, including databases, big data tools such as Apache Hadoop and Spark, cloud storage services such as BigQuery and Redshift, and third-party APIs.

Running on the choice of cloud - Data storage and compute scales resources up and down as per need i.e. manually or automatically.

Manage and control costs - Faculty Platform provides administrators a real-time view of compute costs. It’s simple to view all Faculty servers in use at any one time, as well as their server status and any costs they have incurred. It makes tracking expenditure very simple. If needed, administrators can terminate servers in the admin console and can choose whether to monitor spending based on the whole project or individual users.