Data Labelling

Engineers and scientists across all sectors are striving to prepare vast amounts of data for AI. In the realm of autonomous vehicles, teams specializing in computer vision utilize annotated data to develop and train algorithms that help the vehicles identify pedestrians, trees, signs, and other cars. Meanwhile, by leveraging tagged data and natural language processing (NLP), data experts are automating the review of legal contracts and identifying patients more susceptible to long-term diseases.

The effectiveness of these systems hinges on proficient human involvement, who meticulously label and organize the data for machine learning (ML). Superior data leads to enhanced model outcomes. Conversely, if the data is poorly labeled, the ML model will face challenges in its learning process.

A study by the research firm Cognilytica reveals that roughly 80% of the time dedicated to AI projects is consumed in collecting, refining, tagging, and enhancing data for ML models. Only a fifth of the project duration is allocated to tasks like crafting algorithms, fine-tuning models, and implementing ML. Central AI undertakings demand strategic planning and advanced expertise in fields like engineering or computer science. Therefore, it's prudent to assign more costly human assets, such as ML engineers and data scientists, to roles demanding specialized knowledge, teamwork, and problem-solving capabilities.

Our Data Mechanism

The AGIE Data Mechanism offers a comprehensive suite of tools and functionalities necessary for gathering, refining, and marking data, as well as for assessing models for enhancement. The advanced LLMs and generative models thrive on the power of AGIE's Generative AI Data Mechanism, fortified by top-tier RLHF, data production, model assessment, safety measures, and alignment.

Data Refinement Discover the most crucial data by smartly overseeing your data collection.

The AGIE AI collection of data management, testing, model assessment, and model juxtaposition utilities empowers you to focus on what's essential. Elevate the effectiveness of your labeling budget by pinpointing the most important data to mark, even in the absence of baseline labels.

Supported Annotation Types

Text

• Classification
• Named Entity Recognition
• Transcription
• Ranking
• Rating
• Generation
• RLHF
• Comparison
• Prompt-Response Pairs

Video

• Bounding Box
• Classification
• Cuboid
• Ellipse (Multi-Geometry)
• Lines & Splines
• Point
• Polygon
• Segmentation

Image

• Bounding Box
• Classification
• Cuboid
• Ellipse (Multi-Geometry)
• Lines & Splines
• Point
• Polygon
• Segmentation

Audio

• Classification
• Transcription