Challenges in Machine Learning Data Annotation & How to Overcome Them
Artificial Intelligence and Machine Learning are rapidly growing technologies giving rise to unbelievable inventions—such as procurement optimization, product recommendations, refined search engine results, autonomous drones, and self-driving cars. These AI/ML-based models and applications are delivering advantages to several fields globally. And, to develop such smart machines or applications, a huge amount of training data sets is expected consistently, highlighting the importance of data annotation in Machine Learning.
Machine Learning data annotation is the process of adding tags and labels to the data accessible in different formats such as video, text, or images. These tags and labels provide the context to help train the machines. Using this additional information, the AI/ML models understand their environment and calculate the attributes easily.
Labeled data sets are expected so that Machine Learning algorithms can easily and clearly comprehend the input patterns during supervised learning. In other words, high-quality, accurate, and relevant training data helps build and improve Machine Learning applications across industries and verticals.
Understanding the Significance
We all know that computers are competent at delivering ultimate outcomes that are not only exact but related and timely as well. So, a question worth wondering here is, how does a machine learn to provide such efficiency?
Well, all thanks to the process of data annotation. When AI/ML algorithms are under improvement, they are fed with volume after volume of accurately labeled training data to help them in making unbiased judgments and identifying different elements or objects.
It is only through the data annotation process that machines can distinguish between a dog and a cat, a sidewalk from a road, or an adjective and a noun. Without appropriate data annotation, every impression would be the exact same for computers as they do not have any ingrained information, prior experience, or understanding about anything on the planet.
Data labeling is expected to help modules specify elements to equip computer vision and speech, make networks deliver detailed results, and recognize objects. Data annotation is responsible to ensure that the decisions are relevant and accurate for any model that has a machine-driven decision-making system at the fulcrum.
Fundamental Challenges in Data Annotation
It requires dedicated efforts and time from highly skilled and competent professionals to label datasets accurately. Data must be properly organized, structured, and labeled before being fed into the Machine Learning algorithms. Some of the fundamental challenges faced by companies include:
Any AI/ML-based model’s outcome is as accurate as the data it is fed with. Human errors and/or omissions can lead to poor data quality and immediately impact the AI/ML models’ outcomes. One of the recent Gartner reports states that poor data quality costs organizations an average of $12.9 million.
Hiring and training resources who can perform the data annotation tasks accurately is a significant undertaking. It not only involves a tiring recruitment process but adds up to operational expenditures. Besides, there is a shortage of skilled annotators in the field as it is relatively new, which convinces organizations the other way, i.e., to engage in data annotation services.
Technology & Infrastructure
Creating an infrastructure that supports the data labeling process and maintaining the same requires a budget. Any technical infrastructure consists of the development, up-gradation, and maintenance cost. Enterprises that are not into core technical services often find this as a financial liability and resort to outsourcing data annotation projects.
Accurate data labeling is the key to getting accurate AI/ML outputs. If data annotation misses the mark, it will lead to similar errors in AI as in humans. Skilled administrative professionals are required to supervise these annotation tasks and to ensure that data is labeled accurately. Hunting for such talent is a typical challenge, more so when it affects productivity directly.
The data labeling department needs adequate infrastructure and appropriate technology along with human resources—setting all this up involves costs and time. Knowing the advantages of data annotation, many industry leaders are reluctant to upgrade their workflows to their usage because of these challenges. Consulting professionals is another option they can consider, though brainstorming on eligible data annotation companies is necessary before jumping into action. The process can be performed automatically or manually. Nonetheless, manually annotating data needs a lot of effort, and you must also maintain the data integrity.
Data labeling is a vital job where different scenarios have different data annotation requirements. For example, a self-driving vehicle will operate on a different algorithm in comparison to a drone. This industry is highly reliant on manpower, currently, annotation experts rely on tools and techniques to label datasets.
Whether to outsource data annotation or go in-house depends on the complexity of the project too. If you have a limited labeling requirement or are very much concerned about the privacy of the data, you might be convinced to get an in-house setup. On the other hand, large projects may require a lot of bounding box annotation or semantic segmentation. In such a case, it would be better to collaborate with experienced data annotation service providers that know what it takes to achieve your goals.
Read inspired blog here: https://www.datasciencesociety.net/challenges-in-machine-learning-data-annotation-how-to-overcome-them/
