Our Internal Active Labeling Engine for Maximum Efficiency

Label-In-The-Loop is an advanced active learning and Human-in-the-Loop toolset used internally at QED Software. We leverage this technology to fundamentally change our data curation process, drastically reducing labeling costs and time while achieving maximum model quality and accuracy for our clients.

How it benefits your projects

We use Label-in-the-Loop to tackle key data-centric project challenges:

• LITL automates the process of interactive data labeling, based on the detection of relevant examples in large data sets and their ergonomic designation to domain experts operating within the organization. 
• LITL makes it possible to identify higher-level concepts in multimodal datasets and obtain immediate ML models while experts work. LITL facilitates semantic indexing of unstructured data and data and model quality maintenance.

Value delivered by Label-In-The-Loop technology

LITL is our internal mechanism that guarantees a competitive edge in delivered solutions:

• Delivering immediate ML Models. Completing the data labeling task while simultaneously delivering a readily-integrable, reference Machine Learning model.
• Discovering higher data concepts. Utilizing complex relationships and concepts hidden in multimodal data to elevate project sophistication.
• Maintaining data and model quality. Performing rapid audits of data and model health, allowing for immediate corrective action.
• Achieving optimal model with minimal input. Focusing on the goal: building the best possible model with the smallest number of labeled samples.

Our internal LITL operational process

 automates the interactive cycle, seamlessly integrating human experts and artificial intelligence:

1. Intelligently selecting samples. Our LITL AI identifies and provides experts with only the most influential and "hardest" data samples for review.
2. Automatically labeling easy data.LITL continuously builds and updates models, using them to automatically label the "easy" and obvious parts of the dataset.
3. Capturing expert concepts. Experts can label any data they deem important, allowing LITL to identify higher-order concepts within complex, multimodal data.
4. Instantaneously using new data. Newly labeled data becomes immediately available for further analysis and rapid decision-making.