Annotation in Machine Learning – The Key to Fueling Smart Models
Table of Content 1. Introduction 2. Why Professional Data Annotation Services? • • • • •
Professional Excellence Technological Competence Data Integrity Industry Compliant Practices Comprehensive Suite of Offerings
3. Conclusion
“We offer customized data annotation services to a wide network of B2B and B2C clients. Having in-depth experience, our annotators develop enhanced training sets to be fed into machine learning algorithms”
Introduction Supervised training—a strategic combination of human expertise and the latest tools—is required for an AI/ML model to act and make decisions, highlighting the need for data annotation in machine learning.
Why Professional Data Annotation Services? 1.
Professional Excellence
2.
Technological Competence
3.
Data Integrity
4.
Industry Compliant Practices
5.
Comprehensive Suite of Offerings
Professional Excellence The accredited annotators have proper model-behavior understanding and develop training sets according to their future use case, thereby helping the AI/ML models easily identify the objects.
Technological Competence The professionals can efficiently process huge qualities of raw data, remove biases and errors; thus creating a constant stream of structured data sets.
Data Integrity The professional providers follow stringent data security protocols. Having multi-layer security check systems in place ensures that the data integrity remains intact.
Industry-Compliant Practices Companies dealing with data must strictly adhere to various data-related laws including HIPAA, GDPR, PII, etc. The outsourcing data annotation companies stay updated with all such rules and regulations. All their practices are legally compliant and abide by the setindustry standards.
Comprehensive Suite of Offerings The external providers provide a comprehensive suite of offerings including text annotation, video annotation, image annotation, audio annotation, etc— everything under one roof.
Conclusion
In a nutshell, annotation in machine learning is important to help AI-based models grow. Engaging in professional services enables the stakeholders to get constant access to voluminous streams of training sets without compromising on the quality of data.
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