Data annotation is a specialized skill that requires training and experience. But businesses often struggle to find enough qualified annotators to meet their data annotation needs due to a lack of skilled annotators. Moreover, data annotation requires high accuracy and consistency, so even minor errors can significantly impact the data quality. Amidst combating challenges surrounding the complex and time-consuming data annotation processes, businesses often need to scale their data annotation efforts quickly as their data needs to be aggregated to improve the accuracy of the models. That’s why training datasets are essential for learning and performing well for machine learning (ML) models.
Virtusa’s technology-driven and human-assisted data labeling and data annotation services can help you get the best results from your artificial intelligence/machine learning (AI/ML) and deep learning models. Our team of experts can help you with all aspects of data labeling, from creating a data labeling plan to managing and quality-checking your data. We also offer various data annotation services, including image annotation, text annotation, and video annotation.
Get the best results from your AI/ML and deep learning models using Virtusa’s technology-driven and human-assisted data labeling and data annotation services.
As the company’s core strength, Virtusa’s global talent acquisition team gives us access to a range of skillsets, locations, languages, and cultural nuances, at a rapid pace. We use crowdsourcing to get data annotated by many highly skilled raters. Our rich experience working with high-tech clients has enabled us to adopt and implement our robust quality management framework to set baseline expectations with raters, followed by diligent monitoring of individual output. Our dedicated quality assurance (QA) team is well-versed in six sigma, lean, and audit principles, helping us set a high bar in exceeding service level agreements (SLAs).
- Active learning to train a model to annotate data more accurately
- Transfer learning to apply knowledge from one task to another
- Domain-specific knowledge to improve the accuracy of data annotation
- Human-in-the-loop methods to ensure the quality of data annotation