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How Model Predictions are used to Increase Data Labeling Speed and Accuracy

To know how model predictions are used to increase data labeling speed and improve accuracy check this presentation that shows the types of use cases of model predictions in machine learning with various set of examples how fully automatic and semi-automatic labeling process is used to train while model predictions. Posted by Cogito, this blog post is also showing how efficiency of predictions evaluated and how it helps to improve the accuracy of model predictions in machine learning or artificial intelligence based project training. <br> <br>Visit: http://bit.ly/2T6BsVA<br>

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How Model Predictions are used to Increase Data Labeling Speed and Accuracy

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  1. How Model Predictions are used to Increase Data Labeling Speed and Improve Accuracy?

  2. Organizing training data sets in right manner is one of the most complicated processes into machine learning that requires a strict and disciplined curating by experts.

  3. SEMI-AUTOMATIC LABELING The best part of semi-automatic labeling is that predictions are used to pre-label data and as per the research and experiments, semi-automatic labeling can outperform manual labeling across bounding boxes and polygon shapes with better results. Though, correct prediction is also much faster than manually labeling of data but when a prediction is wrong, it is often but not always faster to correct a label rather than label it from scratch. And the types of labeling configuration can affect the predictions.

  4. How efficiency of predictions evaluated? Thus, the answer for the last line in previous Para is right here. To get the answer of this question we need to evaluate the efficiency of using predictions on you own project you first need to decide how often your model is correct. And the next step is measuring the changes in labeling speed for an accurate label and incorrect label.

  5. QA PRODUCTION MODELS – AI-enabled Fully Automated Predictions The fully functional machine learning application is believed to be one that makes decisions independent without human interference. In this case the labeling process is entirely automated and all predictions would be decisions and this workflow chart would compress into a single step. However, such models operating in the real are not 100% accurate every time. While AI- based applications only reduce the need of human input but not eliminate completely.

  6. Summing-up In the whole discussion we got to know how predictions can be used to increase the speed of labeling and assure the prediction accuracy in production line. As compare to traditional labeling process where the labeling was purely done manually by humans, the predictions introduce machine- driven automation into the training data loop.

  7. Cogito is the providing such labeled training data sets use into machine learning for accurate predictions.

  8. Thank You Cogito +1 516 342 5749 info@cogitotech.com www.cogitotech.com

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