TRUST YOUR AI WITH

VDML addresses unique challenges and uncertainties in systems employing ML. These obstacles include:

1. Data quality and integrity – ML models rely heavily on data for training and validation but the data may contain errors, biases, or inconsistencies.
2. Model performance in different contexts – ML models may struggle when faced with different or unseen data, failing to adapt to new scenarios or environments.
3. Edge cases and sparse data – These cases can be difficult to handle as the model may not have sufficient training examples to make accurate predictions.
4. Bias and fairness – ML models can inadvertently learn biases present in the training data. Addressing bias and ensuring fairness in ML models is a critical test in the field.
5. Interpretability and transparency – ML models can be complex and difficult to interpret. Understanding why a model makes certain predictions or decisions can be problematic.
6. Security and privacy – ML models can be vulnerable to attacks where malicious actors manipulate data. Ensuring the security and privacy of ML systems is an ongoing challenge.

VDML integrates with ModelOps and DataOps workflows, enabling a seamless integration of ML validation activities into an agile, CI/CD, and DevSecOps methodology. It adopts a data-centric AI approach, iteratively evaluating the performance of models drawn upon target data domains.

Targeting systems that employ ML, VDML addresses unique challenges and uncertainties while focusing on three key stages of VDML:

1. 1. understanding context
   2. resolving limitations
   3. governing behaviour

This approach helps in realising models with sufficient performance and behaviour, while addressing business requirements and other expected quality characteristics.