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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

NeoPulse® Framework CPU

By: AI Dynamics Inc. Latest Version: 3.0.8

Use the NeoPulse® Framework to build custom deep learning models using data of all types: numeric, text, image, audio, and video.

Product Overview

Use NeoPulse® to build models for most types of machine learning problems including, but not limited to, sentiment analysis, object detection, object recognition, classification and regression. Novices and experts can easily create AI models, using custom data, with as little as 14 lines of code in the NeoPulse® Modeling Language (NML). Combined with the power of SageMaker, it is possible to rapidly train, deploy and scale models both in the cloud and on devices.

Key Data

Version
By AI Dynamics Inc.
Categories
Type Algorithm
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Usage Instructions

NeoPulse® framework is quite a different product when compared to traditional algorithms on Sagemaker platform. Our application has been adapted to work with Sagemaker platform without loosing any of the features of NeoPulse® framework. Our framework is designed to be a one stop solutions for all your application AI needs. As opposed to the traditional algorithms on Sagemaker which requires the user to pass just the data as input for training, we require an additional file along with the data which is a propitiatory language file with an extension ".nml". We call this file as NeoPulse Modelling Language (NML) file. An NML file is to be thought of a script that can be developed easily with some knowledge of NML. This file contains the relative paths to the data that is to be trained or require inference information, type of the data that is being passed, and many other specification whose explanation can be found in our documentation.

Sample jupyter notebook

To get started quickly we have a sample jupyter notebook created. This notebook prepares the data, trains a models, create live inference endpoint, create predictions and removes the endpoint.

Link to the sample notebook can be found here

Input data formats

The data needs to prepared and stored in a specific format that is explained in the notebook above.

Sample input data

The data needs to downloaded and a csv containing the paths to downloaded data is required. To help with creating this we have script that does all the aforementioned steps. Please find the link to the script here

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

NeoPulse® Framework CPU

Contact NeoPulse® support at: support@neopulse.ai

https://www.dimensionalmechanics.com/ai-developer/support

AWS Infrastructure

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Refund Policy

You can cancel at any time. We do not offer refunds.

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