Smart feedback loop

Our labeling engine is governed by a sophisticated and state of the art technology which
works as an AI supervisor bot. We call it the “Smart Feedback Loop”.

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Computer vision model building is not a linear process, it is an iterative and continuous process. Every iteration’s output gives an outlook on improvement for the next iteration.
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The smart feedback loop automates the whole process, making the overall data pipeline more efficient and easy to manage without missing a beat.

It stores all the activities, remarks, guideline and see the images, videos at pixel level to make computer vision data pipeline automated.
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The feedback loop is a technology to ensure efficient utilization of the AI/ML team's time by optimizing the training data generation process which is possible by automation of data collection, curation and annotation.

At annotation level

It gives annotators feedback where chances are high of the wrong annotation. It will work on the remarks and corrections given by the reviewer. The system will automatically put itself on alert if any remark sare already given to a similar kind of image previously.

At collection level

After the annotation system will produce an analysison subjectivity on the label and give power to the MLhead to better assign their expert HITL(Human in theloop) to work on only subjective labels and objectivelabels can be corrected by normal annotaters.

At data curation level

Based on a pre-trained model or custom production model the system will generate the report on the scenarios in which model accuracy gets reduced and will suggest the kind of image it should annotate in the next batch.

The smart feedback loop will run on various pre-trained models and ML techniques to reduce the effort made by the ML team by simply putting automation in place. This assistive technology will help to reduce the cost, time and chances of human errors. It gives ML teams enough space to experiment more with new methods and ideas.

Smart feedback loop

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