Private Deployments
This document provides an overview of the Private Deployment options and prices offered by BigML. Managed VPCs, Self-Managed VPCs, and on-premises Deployments are explained in detail.
BigML Private Deployments
Consumable, Programmable, and Scalable Machine Learning
Put the power of Machine Learning at the fingertips of everyone in your organization with BigML. Integrate BigML's Machine Learning platform with your enterprise systems, while satisfying stringent data security, privacy, or regulatory requirements. BigML offers flexible Private Deployments that can run in your preferred cloud provider, ISP, or on-premises behind your corporate firewall.
A Company-Wide Machine Learning Platform
BigML provides two options for Private Deployments, both giving immediate access to the most effective Machine Learning algorithms and high-level workflows. See all the features.
BigML Enterprise
Full-scale access for companies ready to adopt Machine Learning across departments with unlimited users and organizations.
BigML Lite new
Fast track to value for companies ready to implement their first production use cases in a robust and efficient way.
Seamless Integration and Automation
Integrate with your company's Service Oriented Architecture and give your developers the utmost flexibility in automating sophisticated Machine Learning workflows end-to-end via BigML's REST API, open-source bindings, command line tools, plus BigML's domain-specific language: WhizzML. Start building transparent predictive applications that are easy and cost effective to develop, deploy, operate and maintain —all on the same platform.
Rock-solid, Enterprise-grade Scalability
Experience unmatched productivity gains by focusing on analytics tasks instead of low level infrastructure management. BigML's robust, distributed architecture has been tested by thousands of customers to create millions of models predictions. Let BigML's linearly scaling, best-of-breed algorithms with built-in traceability and repeatability abstract away the complexities of hardware configurations.
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