Data requirements vary by problem complexity. Simple models may perform well with thousands of records, while deep learning applications often require hundreds of thousands. We assess your existing data assets and recommend appropriate approaches — including transfer learning and synthetic data generation when needed.

We use industry-standard metrics based on your use case — accuracy, precision, recall, F1-score for classification; RMSE, MAE, R-squared for regression; silhouette score for clustering. We also track business KPIs like conversion lift, cost savings, or error reduction to measure real-world impact.

MLOps (Machine Learning Operations) bridges the gap between model development and production deployment. It encompasses CI/CD for models, automated retraining, performance monitoring, drift detection, and governance. Proper MLOps ensures your models remain accurate and reliable as data patterns evolve over time.