The field of AI and machine learning is rapidly expanding and bringing remarkable advancements that benefit various industries worldwide. However, to develop automated machines or applications, a significant amount of training data sets and labelling records are required. This is where data annotation practices are utilized by stakeholders in respected industries to enable computer vision to recognize objects in machine learning datasets.
This process of annotation not only benefits the AI industry but also offers advantages to other stakeholders. In this post, we will explore the main challenges and best practices of data annotation in different fields and why hiring the best data annotation outsourcing service should be your priority for the task.
What is data annotation?
Data annotation involves the process of labelling data in various formats, such as text, images, video, and audio. This is important for supervised machine learning, which relies on labelled datasets to help machines recognize patterns in the data.
To train computer vision or speech recognition machine learning models,accurate annotation of data is essential, and various tools and techniques areused to create these labelled datasets. There are several types of dataannotation methods that can be used to ensure the highest level of precisionand consistency in the labelled data.
Main challenges of data annotation
Maintaining cost-effectiveness and accuracy are the main challenges of data annotation techniques and this is why many businesses prefer to do it by hiring a data annotation outsourcing service from industry experts. Here’s a brief description of the cost and accuracy challenges that many stakeholders face during the process:
- Cost of annotating data
Data annotation can be accomplished either manually or through automated means. Nonetheless, the process of manual annotation is a labor-intensive task that demands considerable effort. Moreover, it is imperative to uphold the quality of the annotated data throughout the annotation process to ensure accurate and reliable results.
- Accuracy of data annotation
The negative impact of human errors on data quality can directly affect the accuracy of predictions made by AI/ML models. In fact, poor data quality is estimated to cause companies a loss of 10-15% of their revenue.
However, the accuracy and cost-effectiveness of a machine learning model are directly proportional to the amount of annotated data used for its training. By incorporating a diverse range of datasets during the training process, the machine learning algorithm can learn various factors that enable it to utilize its database effectively and deliver optimal results in different scenarios.
When you hire an expert data annotation outsourcing service, professionals help you create machine learning-based trained AI models to provide an enhanced and seamless experience to end users. At Infosearch BPO, we provide professional data annotation outsourcing services for a variety of industrial use cases. Contact us to discuss your specific requirements and goals with data annotation & labelling for machine learning.
No comments yet