Kortical

Kortical helps data scientists move from a business dataset to a working machine-learning service with less repetitive infrastructure work. It combines exploratory analysis, cleaning, feature engineering, transparent AutoML, large experiment runs, model explanations, deployment, application building, and automated retraining through both visual and code-based workflows.

Automated model selection does not prove that a system is fair, causal, or safe. Define the target and decision impact, prevent data leakage, reserve a representative test set, inspect explanations and subgroup errors, document transformations, secure deployed endpoints, and require domain review before a prediction affects customers or operations.