Human-in-the-loop or HITL is defined as a model that requires human interaction. Basically, Human-in-the-loop is the branch of artificial intelligence that combines both human and machine intelligence to create machine learning models.
The HITL approach combines the best of human intelligence with the best of machine intelligence. Machines can make smart decisions from large datasets, while people are much better at making decisions with very little information. Humans also provide labeled data for model training and that’s one of the most important tasks in the AI system. To power Machine learning we use Advance Data labeling Techniques that improve the quality of training data in an interactive manner after human correction takes Less time and greater output. Nothing is more essential than quality data in Machine Learning.
Human in the loop is used in many use cases covering NLP, computer vision, sentiment analysis, transcription, etc.
Humans + Machines = Trained Machine Learning Algorithms
Here, the data labeling process is the first step in creating a an accurate model through algorithms.
For proper results and accurate ML algorithms, a sufficient amount of datasets is must to make proper decisions & so machines require quality datasets.
Quality Datasets = Human + Machine intelligence
- Humans Label Data
- Gives model high-quality Data
- ML algorithms make decisions from this data
- Then Humans Tune the model
- Last people can test and validate a model via output
YOUR DATA OUR RESPONSIBILITY
Learning spiral, Data Labeling company has a workforce with a diverse set of skills and the ability to deliver data annotation and data labeling at scale. Learning Spiral enables businesses and organizations to get work done easily and quickly when they need it. Our affordable data annotation services provided by trained in-house dedicated professionals will ensure customized annotation services and high quality labeled data to meet your needs.