Data Annotation in Machine Learning – Key Challenges and How to Overcome Them

Data Annotation in Machine Learning

Data Annotation in Machine Learning –

Key Challenges and How to Overcome

Them

Introduction

Smart equipment, features, and applications have made our lives smarter. Right from nudge replies to emails to self-driving cars, estimating the time of arrival via GPS to the next song in the streaming queue—everything is powered by Machine Learning and Artificial Intelligence.

To perform such actions, smart models are to be fed with data; a lot of training data as it forms the backbone of AI and ML algorithms. This is because machines can’t process information the way human brains do. They have to be told what they are interpreting and need context to make decisions and perform the desired actions. And, it is the data annotation process that makes those connections.

In practice, data annotation is the human-led task of labeling specific data including text, images, audio, and videos to make it easier for Machine Learning algorithms to detect, identify, and classify information like humans do. If data isn’t labeled, computers won’t be able to calculate the essential attributes.

Challenges in Machine Learning Data Annotation

Applications of Artificial Intelligence and Machine Learning platforms are becoming commonplace for businesses. Yet, a thick layer of overhyped and fuzzy jargon shadows the challenges faced by companies looking to implement AI and ML-based models. Some of these are listed here:

• High-Quality Training Datasets

• AI/ML Projects are Data Hungry

• Cost of Project Completion

Key Advantages of Annotation in Machine Learning

For the Machine Learning algorithms to perform better, data annotation is the key as it provides a context and a deeper understanding of the objects. Collaborating with professional data annotation companies helps businesses to enjoy a gamut of advantages as listed here:

• Improved Precision

• Streamlined End-User Experience

• Progressive AI Engine Reliability

• Imparts ability to Scale Implementation

Conclusion

The right application of data annotation is only possible when businesses leverage the strategic combination of human intelligence and the latest technologies to create high-quality training data sets for Machine Learning algorithms. Companies must build strong data annotation capabilities to support their AI/ML project building and prevent it from failing miserably.

Accurately labeled data determines whether you created a high-performing AI/ML-based model as a solution to a certain business challenge, or wasted time and efforts on a failed experiment. So, when lacking resources and time to build such capabilities, collaborating with experienced data annotation companies is a smart move. Apart from time and cost optimization, professional providers allow you to rapidly scale your Artificial Intelligence capabilities and conceptualize Machine Learning solutions to meet customer expectations and match the market requirements.