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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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!
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.
