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.

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.

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.

How it helps-

 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 remarks are already given to a similar
kind of image previously.

At collection level

After the annotation system will produce an analysis on subjectivity on the label and
give power to the ML head to better assign their expert HITL(Human in the loop) to work
on only subjective labels and objective labels 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.

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