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Executive Summary
Infosearch BPO being one of the most successful business process outsourcing companies offers highly effective annotated service, which is perfect for universities and the AI research centres. This paper presents a description of how Infosearch BPO has worked together with academic institutions and research organizations to improve data annotation for the development of machine learning and artificial intelligence.
Introduction
As the need for accurate annotated datasets rises in developments in the field of artificial intelligence, universities and research laborations are finding it difficult to access, organize, and process large volumes of data adequately. With help of Infosearch BPO that specializes in annotation services were provided that involves data processing and quality assurance.
Objectives
1. The purpose of the designing modular system is to simplify the process of data annotation for the organization of academic institutions and AI research laboratories.
2. To enhance the standard and reliability of annotated datasets.
3. To allow researcher to concentrate on critical activities while leaving menial jobs to third party service providers.
Services Offered
Infosearch BPO provides a comprehensive suite of annotation services, including:
Implementation - Many Universities and Research Labs are joining in the development of the project.
Currently, Infosearch BPO has a cooperation with several well-known universities and AI research centres. The implementation process typically involves the following steps:
1. Needs Assessment: It is important to consider certain properties and characteristics as well as type and size of data, objectives and goals of the institution.
2. Project Planning: Writing a schedule and a work breakdown structure that defines the following: schedule, resources, and quality.
3. Annotation Execution: Each annotation task was carried out by a team of annotators who were trained professionally in different fields.
4. Quality Assurance: They underwent strict procedure of quality assurance of the annotated data they produced.
5. Feedback and Iteration: Working with clients to adapt concepts according to what has been said and what is likely to be expected in each project.
Technology Utilization
In an annotation, Infosearch BPO uses specialized tools and applications so that the company is agile in its operations. Through the machine learning assisted annotation tools, increasing the speed of the tasks is beneficial while at the same time matching the high standards of quality.
Case Example: University of XYZ (Off Record)
Background
A large set of such unannotated image databases became problematic for the University of XYZ, which hosts an AI research program well noted for a specific project involving computer vision.
Solution
Image annotation services were completed in cooperation with a partner company – Infosearch BPO – which worked with the university. By being guided by the above steps, the BPO team was able to annotate over 100, 000 images in a time span of three months.
Results
Challenges Faced
Conclusion
Annotation services provided by Infosearch BPO have greatly benefitted universities, and AI research laboratories where teams experienced need for high-quality data processing. This way the company does not just improve the efficiency of researches also promotes the development of AI solutions. In the future, Infosearch BPO intends to open new services to cover new needs of academic and research institutions, with the incorporation of new technologies.
Future Directions
This paper focuses on how the annotation services offered by Infosearch BPO have contributed to enhancing academic research and enhancing AI technology while presenting a viable model for other similar research collaborations in the future.
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