Power your Data for AI to accelerate your model deployment.
Efficiently build machine learning models and use them to make highly accurate context-based predictions in minutes without writing any code!
The DataNeuron Pipeline
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How it Works ?
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/img/animation/upload/upload1.svg
Data Ingestion

Users can upload the data without any pre-processing.


ALP has an in-built feature that can handle out-of- scope paragraphs and separate them from the classification data. This functionality is optional and can be toggled on/off anytime during the process.

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Dynamic Masterlist

On the masterlist, attributes can be defined and structured in a multi-level (hierarchical) structure so that the data can be grouped into domains and subdomains.


Masterlist Suggestions to prepare better training data. Masterlist can be continuously managed and tweaked based on new attributes in the same dataset.

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Explainable Data Validation

The ALP performs guided and automated annotation. The platform then provides the users with a list of annotated/labelled paragraphs that are most likely to belong to the same class by using context-based filtering and analysing the masterlist.


Strategic Annotation - to achieve the target with higher accuracy while capturing multiple data points in every attribute with lesser annotation.

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AutoML

DataNeuron automates pre-processing, model creation, validation of the accuracy check and confidence level.


Additionally, the platform efficiently generates a Summary Report on the training accuracy for every single attribute on the masterlist.

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Export, Deploy & Predict.

DataNeuron’s prediction service provides highly accurate context-based predictions on the ingested data in near real time without writing any code.


Prediction Service can be integrated with various applications through the supporting APIs.

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Iterate the Process

Continue to the deployment stage if the trained model is able to match the expectations


If the model does not achieve the desired results, the user can choose to go back and provide more training paragraphs (by validating more paragraphs or uploading seed paragraphs) or alter the project structure to remove some classes and then retrain the model to achieve better results.

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DataNeuron’s Intuitive Interface

Intuitive Interface for SME collaboration for AI/ ML model deployment. DataNeuron is also a secured platform for data exchange/model creation.


The interface features a low learning curve that even allows teams without data scientists or ML Engineers to be able to use the platform to its maximum potential without effort!

/img/animation/upload/upload1.svg
Data Ingestion

Users can upload the data without any pre-processing.


ALP has an in-built feature that can handle out-of- scope paragraphs and separate them from the classification data. This functionality is optional and can be toggled on/off anytime during the process.

img/animation/masterlist/master1.png
Dynamic Masterlist

On the masterlist, attributes can be defined and structured in a multi-level (hierarchical) structure so that the data can be grouped into domains and subdomains.


Masterlist Suggestions to prepare better training data. Masterlist can be continuously managed and tweaked based on new attributes in the same dataset.

img/animation/validate/validate1.png
Explainable Data Validation

The ALP performs guided and automated annotation. The platform then provides the users with a list of annotated/labelled paragraphs that are most likely to belong to the same class by using context-based filtering and analysing the masterlist.


Strategic Annotation - to achieve the target with higher accuracy while capturing multiple data points in every attribute with lesser annotation.

/img/animation/train/train1.svg
AutoML

DataNeuron automates pre-processing, model creation, validation of the accuracy check and confidence level.


Additionally, the platform efficiently generates a Summary Report on the training accuracy for every single attribute on the masterlist.

/img/howitworks/predict.svg
Export, Deploy & Predict.

DataNeuron’s prediction service provides highly accurate context-based predictions on the ingested data in near real time without writing any code.


Prediction Service can be integrated with various applications through the supporting APIs.

/img/howitworks/process.svg
Iterate the Process

Continue to the deployment stage if the trained model is able to match the expectations


If the model does not achieve the desired results, the user can choose to go back and provide more training paragraphs (by validating more paragraphs or uploading seed paragraphs) or alter the project structure to remove some classes and then retrain the model to achieve better results.

/img/interface.svg
DataNeuron’s Intuitive Interface

Intuitive Interface for SME collaboration for AI/ ML model deployment. DataNeuron is also a secured platform for data exchange/model creation.


The interface features a low learning curve that even allows teams without data scientists or ML Engineers to be able to use the platform to its maximum potential without effort!

/img/animation/upload/upload1.svg
Data Ingestion

Users can upload the data without any pre-processing.


ALP has an in-built feature that can handle out-of- scope paragraphs and separate them from the classification data. This functionality is optional and can be toggled on/off anytime during the process.

img/animation/masterlist/master1.png
Dynamic Masterlist

On the masterlist, attributes can be defined and structured in a multi-level (hierarchical) structure so that the data can be grouped into domains and subdomains.


Masterlist Suggestions to prepare better training data. Masterlist can be continuously managed and tweaked based on new attributes in the same dataset.

img/animation/validate/validate1.png
Explainable Data Validation

The ALP performs guided and automated annotation. The platform then provides the users with a list of annotated/labelled paragraphs that are most likely to belong to the same class by using context-based filtering and analysing the masterlist.


Strategic Annotation - to achieve the target with higher accuracy while capturing multiple data points in every attribute with lesser annotation.

/img/animation/train/train1.svg
AutoML

DataNeuron automates pre-processing, model creation, validation of the accuracy check and confidence level.


Additionally, the platform efficiently generates a Summary Report on the training accuracy for every single attribute on the masterlist.

/img/howitworks/predict.svg
Export, Deploy & Predict.

DataNeuron’s prediction service provides highly accurate context-based predictions on the ingested data in near real time without writing any code.


Prediction Service can be integrated with various applications through the supporting APIs.

/img/howitworks/process.svg
Iterate the Process

Continue to the deployment stage if the trained model is able to match the expectations


If the model does not achieve the desired results, the user can choose to go back and provide more training paragraphs (by validating more paragraphs or uploading seed paragraphs) or alter the project structure to remove some classes and then retrain the model to achieve better results.

/img/interface.svg
DataNeuron’s Intuitive Interface

Intuitive Interface for SME collaboration for AI/ ML model deployment. DataNeuron is also a secured platform for data exchange/model creation.


The interface features a low learning curve that even allows teams without data scientists or ML Engineers to be able to use the platform to its maximum potential without effort!

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Why choose DataNeuron?
Saves Time
Saves Time
Up to 95% Reduction in time spent performing data labelling.
Saves Money
Saves Money
Up to 85% cost reduction in time saving.
Reduces Effort
Reduces Effort
Up to 96% reduction in data labelled by a workforce.
Increases Revenue
Increases Revenue
Experience an increase in ROI, up to 500%.
Use Cases
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Automated Data Labelling
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Document Classification
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Sentiment Analysis
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Intent Classification
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Contextual Search
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Hierarchial Text Classification
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Knowledge Management
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Topic Detection