Model Validation

Careful cross-validation of algorithmic results to ensure accuracy, reliability, and organizational value is being delivered Mean Squared Error, AUC, ROC, Precision and Recall, Confusion Matrix, F1 Score

Algorithm Prototyping

Fitting models to the training data based on desired projections, data structure, performance metrics, and explainability Classification, Regression, Reinforcement Learning, Unsupervised Learning, Deep Learning, Ensemble Models


Any good ML application starts with the desired outcome or decision to be modeled. Mosaic works closely with our customers to understand their operation, data, Workshops/interviews with stakeholders-data understanding-use case prioritization-ML dev plan created