Almost every organisation engaged in the process of developing machine learning algorithms dabbles with the idea of setting up an in-house team for data annotation requirements. Companies feel that assigning the seemingly easy task of data annotation to their employees will save them of both time and money.
Also, these companies assume that banking on the internal resources will maintain the security and safety of their confidential data. But most of the claims of these companies regarding the benefits of the inhouse option versus outsourcing are unfounded when their machine learning project grows in scale.
In this blog, we put forward a few vital aspects regarding data annotation and how unlike the in-house approach, outsourcing has these aspects integral to it.
When a company decides to go ahead with data annotation outsourcing, it signs itself for:
Superior Quality Training Datasets
Even the most sophisticated of machine learning algorithms turn useless if they are trained on the poor quality of data. Accuracy and quality of training data are of utmost importance for any machine algorithm to work on desired parameters.
This is where outsourcing companies like Suntec, with their professional and experienced workforce, offer a distinct advantage.
Outsourcing companies have access to niche and advanced technologies and custom-built annotation platforms. Since this is their domain and they do this job 24*7*365 days, annotation outsourcing companies have set workflows and guidelines to undertake annotation projects of any scale and complexity.
All these factors ensure that outsourcing companies operate with high levels of accuracy and speed while maintaining quality.
Highly Scalable and Flexible Data Annotation Solutions
To make sure that the machine learning algorithm gives precise results as desired, it must be continuously fed with accurately annotated large training datasets. Also, the evolving nature of machine learning-based models always requires solid backing of training data. Companies opting for inhouse data annotation face inadequacy of resources both in terms of skill and manpower. Also, shifting the staff from their core work to undertake annotation projects affects their output and efficiency.
Outsourcing companies have the capacity and resources to meet the data and workforce requirements of any scale at short notice.
The ability of annotation outsourcing companies like Suntec to adapt to unique requirements of data annotation projects while maintaining the quality and accuracy makes them a go-to option for companies.
Fast and Reliable Annotation Services
Projects in the technical world are time-bound, time becomes an even more critical factor when dealing with ML projects. While outsourcing companies with their established workflows, annotation mechanisms and trained staff guarantee swift services, internal teams working on data annotation projects in all probability will fail to complete it within time.
Since in house employees need some sort of training before they can begin annotation tasks and they also have full-time work obligations, they cannot match their dedicated and specifically trained counterparts in outsourcing companies.
Also, outsourcing companies can quickly manage data contributors and annotators from diverse linguistic and cultural backgrounds to meet the requirements of specific projects like natural language processing (NLP) based ML projects.
Zero Bias
One of the biggest advantages along with the quality and quantity of outsourcing the data annotation project is the removal of bias. Bias occurs when machine learning (ML) algorithms give faulty or prejudiced results due to deficiencies of one or more types in training data. Generally, any training data can be infected with any of the following three types of bias:
Sample Bias
This bias happens when training data doesn’t represent the actual environment which the algorithm would operate in.
While it’s nearly impossible to generate the training data that would completely represent the reality, outsourcing companies like Suntec, with the resources at their disposal are able to provide comparatively more realistic training data.
Prejudice bias
When training data is influenced by cultural, sexual and other stereotypes, it is said to be affected with prejudice bias. Outsourcing companies have a diverse workforce and necessary checks and balances put in place to eliminate this bias.
Internal bias
This bias happens when companies use their internal team for their annotation project. Company employees have certain preconceived or desired expectations regarding the way algorithms would function. So their training data unknowingly gets designed based on a preconceived outcome.
Fool-proof Security
Safety and security of data are of the utmost priority for companies. Some companies are reluctant to outsource their data annotation project for this single reason only. Companies have their apprehensions on privacy compliance like PHI or PII and other similar considerations.
Professional outsourcing companies operate with widely accepted guidelines on ethics and integrity. Owing to their high standards and proven track record, outsourcing companies like Suntec have also been certified by the statutory bodies.
All through these years companies like Suntec have shown that they offer world-class, reliable annotation services while working within the extant IT laws and other legal stipulations.
At Suntec, we have helped some of the world’s best in AI and ML in producing highly advanced machine learning solutions. We have the experience, resources and skill to annotate large volumes of diverse data be it text, images or videos. Our customised services are fast, reliable and cost-effective.