Data annotation plays a key role in making sure AI or ML projects are scalable. Conventionally, a human is shown a series of raw, unlabelled data, and is tasked with labeling it according to a set of rules. Automation of labeling adds another dimension to the process and makes the job of an annotator easier and more efficient.
In a special guest feature with Analytics Insight, Glen Ford, VP of Product at iMerit, explains how automation and humans-in-the-loop combine to build a more productive and efficient process of data annotation.
Here are 3 key takeaways from the article:
- Data annotation provides more context to datasets; it enhances the performance of exploratory data analysis as well as machine learning and AI applications to upscale a business. Businesses from agriculture, autonomous mobility, defense, mining, insurance and many other sectors, use data annotation services to gather data and derive insights for better decision making.
- Automation in data annotation includes applying ML to annotate, label and enrich datasets. Automation and humans-in-the-loop combine to provide companies with greater context, quality, and usability. When you have a machine taking the first pass at annotation and letting the human correct it, the model begins to become competent, and it can make predictions of its own. That makes the job of the annotator twice as easy.
- Visual similarity search powered by ML helps data scientists discover and focus on the best data to send for human labeling. For example, when the annotator finds some interesting case, like a stop sign covered in snow that needs to be annotated with a certain classification that the data scientist hadn’t anticipated, similar instances can be searched for.
Read the complete article here: Role Of Automation In Complex Data Annotation
