DataNeuron is thrilled to announce the official launch of the DataNeuron Automated Learning Platform (ALP). The ALP has been strategically designed to accelerate and automate human-in-loop annotation for developing AI solutions. Powered by a data-centric platform, we automate data labeling, the creation of models, and end-to-end lifecycle management of ML.
We are a team of Data Science enthusiasts having first-hand experience of dealing with data analysts, subject matter experts and data scientists to fulfil the labelled data requirements for building highly accurate contextual algorithms for various use-cases. Our aim is to accelerate the development and provide better explainability of AI.
We are also excited to partner with leading venture capitalists, angel investors and strategic advisors in expanding the horizons of DataNeuron.
But why should we switch from human labelling to the DataNeuron ALP? That’s a great question! Based on our findings from the case studies we have conducted, we have found out that using the DataNeuron ALP can reduce the time spent in annotating by a staggering 89.10%, reduce the number of paragraphs validated by 83.55%, reduce the cost expenditure by 77.83% and yield an ROI of an astounding 372.22%.
The DataNeuron Pipeline
Those numbers sound promising but what more can we do on the DataNeuron ALP? Once Again, that’s a great question! Apart from getting accurately labelled data, the DataNeuron ALP can be used to perform no-code prediction. With just a click of a button, the platform can be used to make a prediction on any new paragraphs in exchange for a very minimal fee. This does not require any knowledge of programming and users can utilize this service for any input data from the platform. This can also be integrated into other platforms by making use of the exposed prediction API or the deployed Python package.
As a cherry on top, the DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential. The users only need some knowledge of the domain they are working on and the details of the project and they’re good to go! For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.
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