Real-World Use Cases of Data Annotation in Machine Learning

Real-World Use Cases of Data Annotation in Machine Learning

Table Of Content I.

Introduction

II. Understanding Annotation in Machine Learning III. AI/ML Real-World Use Cases • • • •

Retail Education Healthcare Ecommerce

IV. Conclusion

Introduction If you make a mistake while texting someone, some of your errors are auto-corrected, you know whom to thank! But have you ever wondered how do AI and ML applications work?

If not, we are here to answer this question.

Data annotation in machine learning is the process that fuels AI/ML applications.

Understanding Annotation in Machine Learning Machines cannot make sense out of the raw data. It must be properly structured and tagged for the smart models to comprehend it. Annotation in machine learning is, therefore, the process of adding labels to the input datasets.

These labels are added in the form of tags, meta tags, and descriptions. This helps the machine learning algorithms to learn and grow as well as deliver reliable outputs.

AI/ML Real-World Use Cases It is no surprise that AI has such wonderful day-to-day applications today. However, you must not limit yourself to these.

Listed here are some peculiar and intriguing use cases of AI/ML in different industries and how data annotation fuels those applications: 1.

2.

3.

4.

Retail Education Healthcare Ecommerce

Retail To deliver an elevated user experience, retailers around the world leverage AI throughout their product and service cycle—beginning from manufacturing to after-sales consumer service interactions.

This not only influences their current sales but also proves to be a guide for inventory management.

Education Another amazing use case is Natural Language Processing (NLP). This means that your course material can be translated from one language to other.

Hence, the course contents and subject matters become easily accessible to a wide range of students who can enormously benefit from these.

Healthcare AI paired with ML has numerous use cases in the healthcare landscape. Or, should I say, AI is a boon to the healthcare sector.

The power surgical robots, medical imaging, preliminary diagnosis, etc., are all powered by data annotation in machine learning. It also helps in detecting links between genetic codes and increasing hospital efficiency.

Ecommerce Using Natural Language Processors, businesses can personalize shopping recommendations for their customers and detect fake reviews; thus, improving their annual results and increasing ROI. AI also facilitates visual product search, voice search, automated customer services, and chatbots, etc.

Conclusion To conclude, AI is revolutionizing businesses across different industries and verticals. To be a part of this new-age revolution, organizations need data support for AI and ML. Collaborating with professionals or engaging in data annotation services is, thus, a smart way out.

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