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.
Synthetic data, artificially generated data that mimics real-world data, is a technology that has undergone significant transformation in recent years. Since the dawn of data-driven synthetic data generation with generative models,...
Photo by Roman Kraft on Unsplash As machine learning continues to evolve, the use of Large Language Models (LLMs) has become increasingly prevalent, particularly in complex tasks requiring deep understanding and generation of human-like...
Photo by Isaac Smith on Unsplash Synthetic data bootstrap In the dynamic landscape of organizations high-quality data is a requirement for the development of many solutions - from software testing and validation all the way to Artificial...
