A Robust Enterprise AI Platform
NeoPulse® is an end-to-end artificial intelligence enterprise platform for experts and non-experts alike. Our low code AutoML solution comes with built in MLOps features and can help your company take AI from the drawing board to implementation in weeks, not months.
Dell Technologies + AI Dynamics Solution Brief
Learn how we can help enterprises transform global manufacturing operations while
addressing the industry’s challenges of an aging workforce and shrinking margins.
with governance and auditability built in
AI Dynamics was formed in 2015 on the belief that businesses, regardless of size, should have access to affordable AI solutions. The NeoPulse Enterprise platform is an end-to-end solution, that enables engineers to build deep learning models faster than using off-the-shelf libraries while handling dataset management, model tracking, deployment, and monitoring automatically.
Our trusted AI platform, allows every enterprise to expand their AI capabilities and deploy them across the organization while providing data security and MLOps capabilities. Our platform is flexible enough to work on-premise, in the cloud, or in heterogeneous environments including some ARM systems.
Why NeoPulse® AI Platform?
Excited to See How NeoPulse® Works?
Our Trusted Partners & Customers
Our Trusted Partners & Customers
An Operating System for Enterprise AI
- NEOPULSE -
Simply building an AI isn’t enough – it has to be used.
In order for AI to be used, it has to be accessible by DevOps teams.
NeoPulse enables DevOps teams to manage AI solutions without having to be AI experts.
Using the unique combination of PIMs and Runtimes, AI models can be deployed to hundreds or even thousands of endpoints.
These endpoints can be managed centrally because each runtime exposes a rich set of APIs and the PIMs contain a large amount of metadata describing the AI model, model baseline performance and machine readable instructions on use.
NeoPulse uses artificial intelligence to create artificial intelligence.
It uses a vast database of machine learning algorithms and decides which one to use and how to tune the “hyper-parameters.”
Like human engineers, it gets better with experience using something called “reinforced learning.”
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