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

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

• High-Quality Training Datasets 

  The quality of labeled data decides the fate of every AI/ML project. It is because any model is as smart as the data it is fed with. The Machine Learning algorithms must be fed with accurately annotated datasets to recognize patterns and relationships between variables or perform the tasks they are designed for. Analytics companies, for instance, cannot afford confusion in the classifiers and misaligned bounding boxes. Such mistakes can prove to be disastrous for businesses. Not to forget that the ability of AI/ML-based models to deliver personalization and efficiency is directly relevant to the quality of training data, which must be precisely curated.

• AI/ML Projects are Data Hungry 

  Machine Learning projects that typically require hundreds of thousands or even millions of properly labeled training items to be successful. Although AI/ML projects can vary widely in complexity, they share a common requirement—large volumes of high-quality and accurately labeled datasets to train the model. More the amount of training data they are fed, the more precise and accurate the outcomes.

• Cost of Project Completion 

  Many companies do not have adequate resources to implement AI/ML models in their business lines. The probable reasons could be time constraints, logistical issues, inadequate infrastructure, and so on. Pulling other team members off their core tasks for data labeling proves expensive. Besides, they aren’t well prepared to handle large-scale data annotation projects. The absence of progressive workflows and accurately annotated data hinders the process of developing models that can make accurate predictions and rightly interpret important attributes.

No wonder the global data annotation market is about to skyrocket to US $ 5,331 million by 2030, with a CAGR of 26.6%. (Source: Grandviewresearch.com)

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

• Improved Precision 

  A computer vision-based model operates with different levels of accuracy—when an image with several objects is labeled accurately against an image where objects have not been labeled or poorly labeled. So, the better the label, the higher the precision of the AI/ML model.

• Streamlined End-User Experience 

  Accurately labeled data offers altogether a seamless experience to the end-users of AI systems. An intelligent product addresses and acknowledges the problems and doubts of different users by providing relevant assistance. And, this capability to act with relevance is developed via annotation.

• Progressive AI Engine Reliability 

  The adage that increasing input data volume increases AI/ML model’s accuracy and precision hold true only when there is a perfect data annotation process in place to supplement the smart model with labeled data. So, as the data volumes ascend, the reliability of AI engines also increases.

• Imparts ability to Scale Implementation 

  Data annotation accommodates intents, sentiments, and actions from multiple requests. Professional data annotation services help businesses with accurate training datasets and offer the ability to scale the AI/ML-based models for diverse datasets of any volume.

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.

Got further doubts whether to outsource data annotation in Machine Learning tasks or not? This article will help you clear the air – Outsource Data Annotation Services to Accelerate Product Development.