Big data is not useful unless it is given context and meaning. This involves examining and annotating every part of the data. To add to the task, the data is often in various formats and data types such as raw text, image, video, PDF, tables, blueprints, spreadsheets... And the sheer volume of both structured and unstructured data makes it seem like an impossible task.
Trained ML models can extract valuable data points, and then proceed to annotate this data, bringing context and meaning. Data extraction is possible from any format including raw text, PDFs, images, tabular data, blueprints and more.
Once the data is properly labeled, it opens up use cases such as:
Computer Vision: Object detection, image segmentation, facial recognition.
Natural Language Processing: Named entity recognition, sentiment analysis, text classification.
Geospatial Analysis: Land cover classification, object detection in satellite imagery.
and any kind of downstream analytics applications.
Improved Model Performance: High-quality annotations lead to more accurate and reliable machine learning models.
Faster Development Cycles: Accelerates the development of AI applications by providing annotated datasets.
Cost-Effective Solutions: Outsourcing data annotation reduces the time and resources required for in-house annotation efforts.
Scalability: Easily scale annotation efforts to handle large datasets and evolving project requirements.
