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VDML is a comprehensive framework specifically designed for the accuracy and reliability challenges associated with ML and autonomous systems.

VDML (Validation Driven Machine Learning) is a methodology developed by KJR to guide development of robust and reliable Machine Learning (ML) models. VDML emphasises understanding the limitations of both models and data, and uses iterative validation methods to guide and assess ML model behaviour.

VDML addresses unique challenges and uncertainties in systems employing ML

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 four key stages of VDML:

    1. problem formulation,
    2. validation analysis,
    3. model optimisation, and
    4. production integration.

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

KJR is engineering trusted AI through its VDML methodology.

VDML delivers effective risk assessment, quality assurance, and governance – solutions which can be integrated and governed within a real-world context.

KJR will utilise its VDML methodology to guide you in your development of robust and reliable Machine Learning models to deliver these AI solutions. To find out more, go to vdml.ai


KJR Chief Technology Officer Dr. Mark Pedersen and Founding Chairman Dr. Kelvin Ross have collaborated to produce a thought-leading white paper on Responsible AI.

The white paper titled “Practical Steps Toward Responsible Governance of AI-Enabled Systems” provides several key learnings.

These key learnings highlight the importance of compliance, governance processes, and a systematic approach like VDML in promoting responsible governance of AI-enabled systems.

By clearly defining the benefits and context in which the AI-enabled system is being used, stakeholders can assess risk and set a baseline for evaluation.

Read the White Paper

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AI Assurance & Data Quality

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