Network defects can be detected using Anomaly Detection algorithms. But the algorithms are severely hampered due to the lack of good data. YData can automatically classify the different types of anomalies and synthesize data accordingly to train better predictive models. YData’s Pipelines allow the user to compare, contrast and fine-tune the ML model for the detection job.
The value of information assets has never been greater. Synthetic data has the same statistical and business value as real data, but it is not traceable back to real individuals. This makes it suitable to be monetized without concerns around privacy.
With AIOps, teams are able to tame the immense complexity and quantity of data collected from modern IT environments, prevent outages, maintain uptime and attain continuous service assurance. YData enables the creation of training datasets and models, and seamlessly integrates production ML models and pipelines into existing IT operations.
In the last decade, we’ve increasingly focused on model-centric Artificial Intelligence, building ever more flexible machine learning models. However, a new paradigm shift – Data-Centric AI – is currently revolutionizing the industry, as...
Synthetic data is a cornerstone of Data Centric-AI, an approach that focuses primarily on data quality rather than models. For the past few years, synthetic data gained attention because of a wide range of applications such as data...
In the rapidly evolving Artificial Intelligence landscape, data quality is the lifeblood that fuels the development of accurate and efficient models. However, accessing and acquiring high-quality, diverse, and labeled data can be quite a...
