Machine learning (ML) observability is critical to ensure that ML models are functioning as intended and delivering accurate results. While identifying the source of an error is crucial, a good ML observability system should also provide context behind the error and its resolution. However, there are several challenges that data scientists and researchers face while building ML models. Some of the challenges include:
- Data privacy laws can limit or restrict the usage of sensitive data, preventing ML engineers and data scientists from working with real customer data
- Data skewness, where class imbalance affects model performance, can be corrected by modifying skewed data
- Biased models, where inherent biases in the training data affect model predictions, can be addressed by protecting sensitive classes from societal or selection bias
- Model risk assessment is necessary to identify gaps in the models and anticipate risks better, reducing harm to customers and other stakeholders
Introducing ‘AryaXAI Synthetics’:
Introducing ‘AryaXAI Synthetics’, now with issue resolution added to its already impressive issue identification capabilities.
‘General Adversarial Networks (GANs)’ has been a powerful technique to generate data like images/ text/ voice etc. But GANs can also be used to generate high-quality synthetic data sets. We are using a combination of GANs and statistical models to generate high-quality synthetic data. Users can use AryaXAI synthetics and resolve critical data gaps, test models at scale and preserve data privacy.
For more details on AryaXAI synthetics, and a case study on imbalance data, refer here.