It is imperative to have trained/modelled/annotated data for Artificial Intelligence and Machine Learning projects. The success of the data modelling depends on accuracy and huge volume of annotated data. In simple words more the annotation, high the efficiency of the data modelling.

The industry is facing a big challenge in getting trained data for various reasons. We would like to discuss some of the most important reasons in this article.

Consistency

The most common channel in annotation is crowd sourcing. It’s the concept of engaging independent annotators for data annotation. There is a possibility of getting more resources to deliver high volume but consistency in data is the biggest challenge. These annotators are provided with online training and paid based on the data points. They do not work as a team hence knowledge is not uniform amongst them. This will reflect in data consistency and the end result will be very bad.

Timely Delivery

Timely delivery is the key for data modelling. It’s essential to have a bigger team to annotate high volume data. It’s not possible with many providers and they either outsource the work to another company or struggle with a small team to deliver on time. It’s important to identify suitable annotation company with high in house team to deliver high volume annotations.

Accuracy

The quality of data annotation is another important factor for successful data modelling. The accuracy should be maintained through strict quality practices and processes. There should be clear cut guidelines drafted from training, execution and delivery stages to ensure high accuracy.

Domain and Process Knowledge

The domain knowledge plays a crucial role. The annotators should learn the domain and process requirements clearly to make the difference in annotation tasks. It will be achieved through training and practice. Ideally annotation companies should involve exclusive teams for domain specific tasks.

 

Non Availability of trained data

The world doesn’t have trained data as each one’s requirement is specific and unique. The academic institutions provide trained data which are used for their researches. However it may not fulfill commercial business requirements. Most of the companies doesn’t like to share the trained data as part of data protection.

Cost impact on having own annotation team

This will be a costly affair for data modelling companies to have their own team for annotations. It will be a tough task to hire, train, engage, supervise and quality audit the work done on a regular basis.

We at Infosearch address all the above mentioned challenges. We are a team of 380+ in house, full time employees providing 15+ type of annotation tasks. We execute recurring and short terms assignments. We work with AI, Machine learning, Universities, Retail intelligence, Autonomous vehicle, Agro-tech, Image recognition industries. We have exclusive teams for exclusive tasks. We currently deliver 25 MM data points a month with an additional capacity of 10MM data points. Please reach out to us to provide our services to your business.


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