Generative & Synthetic Data Validation
Synthetic data is information that’s artificially manufactured rather than generated by real-world events. After incorporating real data, a computer simulation or algorithm generates synthetic data in order to train an AI model on desired sector/customer needs.
Machine learning models often show improved performance with a higher load of data included in the training, therefore where ‘real world’ data is difficult to collect, synthetic data is a valid option for aggregating the needed data.
To ensure high quality data output accuracy and values, be sure that you feed your model with clean data and data with accurate and stable related context to the desired subject.
Cleansing refers to the process of identifying and replacing/not using incomplete, irrelevant, or other problematic (‘dirty’) data.
Some of the main reasons why companies highly evaluate and incorporate synthetic data into their businesses are:
- System or product testing, initial small scale datasets
- Creating training data of unusual cases or data that is hard to collect
- Privacy issues
The risk of a bug or glitch in the generation of synthetic data is common. It is an integral part of programming. Incorrect synthetic data can obviously damage your ML models and Algorithm’s performance, therefore an intelligent and effective QA process is an important part of synthetic data generation.
Tasq.ai has created the ultimate QA engine for synthetic data by evaluating and ranking every piece of data generated.
How do we do it?
Our unique, scalable solutions allow for objective human judgments to be aggregated into a single, accurate confidence score that enables the improvement of the synthetic generated data prior to production. Our global digital Tasqers carry out simultaneous Human-in-the-Loop workflows to validate and correct the realism of the data generated by synthetic data generators, without bias.Tasq.ai supports customers in their pre & post production phases in their AI journey, setting our customers miles ahead of the competition!
The result: CI/CD human-in-the-loop workflows for ranking synthetic datasets.
