Improve Your AI/Ml Model's Performance With Human-Powered Data Annotation

Have you ever wondered why companies devote so much time to creating and refining input datasets for their Artificial Intelligence and Machine Learning projects? The answer is quite simple to this question. Considering all the other governing factors being equal, the higher the quality of the input training datasets, the better the smart model will perform.

Whether it is product recommendations, search engine results and procurement optimization, or autonomous drones and self-driving cars—high-quality, human-powered data annotation helps in building and improving Machine Learning applications across different industries and verticals.

Data Annotation in Machine Learning

For a machine learning algorithm to perform the desired task, it needs to draw observations from experience. And this comes in the form of training datasets.

Training a Machine Learning algorithm to understand its environment, make decisions, and perform the desired action requires consistent streams of high-quality training data. The process of developing enhanced training datasets involves accurate labeling for specific use cases, which is known as data annotation.

Putting it simply, data annotation is the process of adding tags and labels to the input datasets to be fed into the Machine Learning algorithm. These labels are added in the form of descriptions and meta descriptions to help the smart models calculate attributes easily. Using this information, the AI/ML models interpret their environment, make decisions, and take action.

A human data annotator’s job is to train the Machine Learning algorithms what outcome to predict. In practice, different significant features of the data are transcribed (in the case of video), tagged (elements of an image and video), and labeled (different parts of text and speech). These are the features that businesses want their AI/ML system to recognize on its own when the real-world data hasn’t been annotated.

Challenges in Machine Learning Data Annotation

As evident, labeling different parts of the input datasets isn’t an easy task. It is a time-consuming and resource-intensive task that requires dedicated effort to be performed efficiently. Any errors or inaccuracies in the data annotation process can deviate from the outcomes. Besides, any AI/ML model is as smart as the data it is fed with. Hence, data must be properly structured and accurately labeled before it is fed into the Machine Learning algorithm.

Some of the common Machine Learning data annotation challenges faced by companies include:

Inadequate Resources

Training smart models require both human expertise and machine intelligence. Known as the human-in-the-loop model, human experience and judgment are used to continually improve the performance of a Machine Learning application. Similarly, the data annotation process needs humans.

Human-annotated data fuels Machine Learning applications. Coming to data annotation, human judgment introduces clarification, intent, and subjectivity. In some ambiguous cases, such as when determining the relevance of an outcome, more than one human is necessary to reach a consensus. Hence, there is a need for subject matter experts and competent data annotators.

Inappropriate Infrastructure

Setting up any technical infrastructure includes development, maintenance, and up-gradation costs. Likewise, creating an appropriate infrastructure that supports the data labeling process and maintains the same demands budget. And, for many companies that are not into core technical services, getting a suitable data annotation setup often becomes a liability.

Poor Quality Assurance

It is important to note that high-quality training data is the lifeblood of AI/ML models— whether it is Computer Vision applications, Deep Learning algorithms, Natural Language Processing (NLP) models, and so on. The outcomes of a smart model totally depend upon the quantity and quality of its training data. The adage “garbage in, garbage out” highlights the importance of quality training datasets in Machine Learning.

Accurately labeled input datasets are the key to getting exact AI outputs. Missing the quality data annotation mark will result in errors and deviate from the desired outcomes. Therefore, it is crucial to ensure the accuracy and quality of data, especially when it impacts the business’s profits and productivity.

Logistical Challenges

Machine Learning algorithms require constant streams of high-quality, precise, and relevant data. Companies need sufficient infrastructures to store such overwhelming volumes of data, to begin, with their data annotation process, and fuel their Machine Learning applications. Then, there are compliance issues such as GDPR to be conformed to. Failing to abide by any of these might lead to serious lawsuits.

Last but not the least, time is another challenge on the list. Once a process system is on the run, introducing a new system to it gets difficult.

Way Forward

To get high-quality training datasets and power their AI/ML projects, growth-focused business players rely on professional data annotation services. Outsourcing such critical tasks to data annotation companies helps organizations in improving the performance of their Machine Learning models—by using a strategic combination of the latest software, verified workflows, and competent annotators to develop quality sets of training and testing data. Hence, you know where to begin!