Data labeling is the process of attaching meaning to different types of digital data like audio files, text, images, videos and more. Once the data is labeled, it’s used for training advanced algorithms to recognize patterns in future similar data sets.
Machine learning is now applied in almost every domain to enrich and enhance the capabilities of the systems. Machine learning algorithms trained/validated on annotated/labeled data. Prediction capability of machine learning applications highly depends on the quality of annotations. Generating high quality annotations is big challenge for building sophisticated machine learning applications.
It’s important to select the appropriate data labeling approach for your organization, as this is the step that requires the greatest investment of time and resources. Data labeling can be done using a number of methods (or combination of methods), which include:
Once you have labeled data for training and it has passed QA, it is time to train your AI model using that data. From there, test it on a new set of unlabeled data to see if the predictions it makes are accurate.
You’ll have different expectations of accuracy depending on what the needs of your model are. If your model is processing radiology images to identify infection, the accuracy level may need to be higher than a model that is being used to identify products in an online shopping experience, as one could be a matter of life and death. Set your confidence threshold accordingly.
We provide data labeling services to improve machine learning at scale. As a global leader in our field, our clients benefit from our capability to quickly deliver large volumes of high-quality data across multiple data types, including image, video, speech, audio, and text for your specific AI program needs.
For more information on DATA LABELING Services, please contact us at info@espirittech.com