Production Deployment
Enterprise production requires different skillset than algorithm development. Bias Monitoring, Drift Alerting, Performance Tuning, Automating Data Pipelines and Outputs
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
Exploratory Data Analysis
Any good ML model needs significant data & feature engineering to get the data ready for predictions/prescriptions Trend ID, Graphing, Visualizations, Correlations, Causations
Collaboration
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