ML Model Audit & Optimisation

At Lumen, we help organisations optimise the performance, accuracy, and fairness of their machine learning (ML) models through a comprehensive audit and optimisation process.

As ML models become increasingly critical to business operations, ensuring they perform reliably, ethically, and at scale is essential for delivering real business value. Our ML Model Audit & Optimisation services ensure your models are robust, interpretable, and production-ready.

Model Performance Evaluation

Assessing the accuracy, precision, recall, F1 score, and other relevant metrics to determine how well your model is meeting business goals and objectives.

Bias & Fairness Assessment

Evaluating models for potential biases in predictions, ensuring that they adhere to ethical standards and regulatory requirements (e.g., fairness, transparency, and accountability).

Feature Engineering & Selection

Reviewing the input features used in model training to ensure that the most relevant, high-impact features are selected and optimised for better model performance.

Hyperparameter Tuning

Fine-tuning model hyperparameters through methods such as grid search, random search, and Bayesian optimisation to achieve the optimal performance.

Model Interpretability & Explainability

Implementing model interpretability tools (e.g., SHAP, LIME) to ensure that models are explainable and their predictions are understandable by stakeholders, especially in regulated environments.

Scalability & Efficiency Optimisation

Evaluating and improving model scalability, ensuring that the model can handle large datasets efficiently without compromising speed or accuracy.

Model Robustness & Generalisation

Ensuring models are robust to changes in data and are not overfitted, enabling better generalisation to unseen data and real-world scenarios.

Model Deployment & Monitoring

Preparing models for production deployment, implementing continuous monitoring and automated retraining systems to ensure ongoing performance improvements and address drift.