Accurate data labeling for AI + machine learning.

Data labeling refers to the annotation process of adding tags or labels to raw data such as images, videos, text, and audio.

These tags form a representation of what class of objects the data belongs to and helps a machine learning model learn to identify that particular class of objects when encountered in data without a tag.

Training data refers to data that has been collected to be fed to a machine learning model to help the model learn more about the data.

Training data can be of various forms, including images, voice, text, or features depending on the machine learning model being used and the task at hand to be solved.It can be annotated or unannotated.

When training data is annotated, the corresponding label is referred to as ground truth.

Before an AI system can identify images or analyze text on its own, it must be “trained” with hand-labeled examples. In the case of self-driving cars, that means manually labeling millions of images and videos.

Let’s imagine you want to train a sentiment analysis model. You’ll need to feed the AI model labeled examples (or “training data”) of positive, negative, and neutral sentiment. And beyond that, you’ll need to include sometimes ambiguous phrases that demonstrate human language at its most complex level, like sarcasm and irony – some of the most difficult sentiments for a machine, or even humans, to detect.

Good quality training data is key to determining the success of AI tools. It must be relevant, free from noise (like errors, duplicates, and irrelevant data) and it must be labeled correctly. Get your training data and labels in order and you’ll be able to rely on this information to improve your products, services, and everyday processes.

Our experienced team takes your project specifications and creates custom procedures designed to maximise success. Your Project Manager is responsible for running the project: writing out the labeling instructions, ensuring the labeling quality is consistent and sourcing expert labelers.

They will be your point person for updates and the achievement of milestones.

Highest-quality annotation of text, images, audio and video data for complex models. Ideal for computer vision, sentiment analysis, entity linking, text categorization, and syntactic parsing and tagging models.

Images | Videos | Object Recognition | Facial Recognition | Satellite Photos | Drone | Vehicle and Traffic | Driving

Not yet but watch this space for more soon! We do have our collation of over 100 voice and visual open datasets.