No experience? No problem! NML 2.0 empowers developers with no AI experience to build their own ML models
Who says you need that data science degree? NML 2.0 and the AI Studio 2.0 empower developers with no AI experience to build their own machine learning models.
This has to be some kind of milestone on the path toward terminators. Developers can now use AI to build their own AI with NeoPulse AI Studio 2.0 and its own programming language, NML 2.0. There’s no need for in-depth machine learning knowledge – with these tools developers can easily utilize the powers of AI.
The rising demand for specialized machine learning knowledge has not gone unnoticed, especially since the supply isn’t even close to catching up. In order to address this growing gap, DimensionalMechanics has launched a suite of tools intended to make things easier for developers everywhere, including a domain specific language, an AI studio, a framework, and a few other helpful tools.
First off, let’s look at the language. NeoPulse Modeling Language 2.0 (or NML for short), is a domain specific language designed to work with the AI Studio. It automates the building of new AI models. The automation can take care of all sorts of common machine learning problems, like regression or classification. No need to worry about data type either – NML can handle all kinds of input from video, audio, images, and more.
NML is comparatively concise and simple. This scripting language allows developers to first describe the ML task at a high level before using any of their prior ML experience. NML has a tight integration with the Tensorboard diagnostic tool.
DimensionalMechanics boasts that NML is so concise, a code snippet will be 85% shorter than one written in Python with Keras. However, as a domain specific language, it is intended to be executed within the NeoPulse AI Studio.
NeoPulse AI Studio
Man, the X-as-a-Service trend never ends! Here, we have AI-as-a-Service. Yeah, you got that right, this the part where an AI helps you build your own AI. The NeoPulse AI Studio has its own internal AI nicknamed “the oracle”. The Oracle helps developers choose the optimal architectures and automates building custom AIs.
If that wasn’t enough, a big advancement from the NeoPulse Framework is next generation hyper-parameter optimization. The AI helps choose which parameters are needed for data analysis, optimizing the whole model. This is how developers can outperform conventional Bayesian optimization approaches. The AI Studio also comes with a CLI to facilitate interaction with the NeoPulse framework.
Additionally, the AI Studio makes it possible to export to portable interference models (PIMs) for deployment everywhere. These containerized neural networks can run on local computers or in the cloud.
SEE MORE: “Machine learning tends to have a Python flavor because it’s more user friendly than Java”
Interested in making machine learning easier for the enterprise? NeoPulse AI Studio and all its assorted accouterments are available via the AWS Marketplace. There is a 30-day free trial, followed by tiered pricing afterwards.