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.

Data Labeling Approaches

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:

• In-house: Use existing staff and resources. While you’ll have more control over the results, this method can be time-consuming and expensive, especially if you need to hire and train annotators from scratch.
• Outsourcing: Hire temporary freelancers to label data. You’ll be able to evaluate the skills of these contractors but will have less control over the workflow organization.
• Crowdsourcing: You may choose instead to crowdsource your data labeling needs using a trusted third-party data partner, an ideal option if you don’t have the resources internally. A data partner can provide expertise throughout the model build process and provide access to a large crowd of contributors who can handle massive amounts of data quickly. Crowdsourcing is ideal for companies that anticipate ramping up toward large-scale deployments.
• By machine: Data labeling can also be done by machine. ML-assisted data labeling should be considered, especially when training data must be prepared at scale. It can also be used for automating business processes that require data categorization.

Train and Test

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.

What We Can Do For You

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