Cortex Model
Build end-to-end ML pipelines from data validation through model deployment
What You Can Do
You can build production-ready ML pipelines that start with data validation and progress through model training, evaluation, and deployment. Cortex detects your ML stack, defines success metrics upfront, builds baseline models before optimization, and generates serving-ready artifacts—eliminating manual workflow steps and reducing time from data to deployed model.
Features
identifies your framework (scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM) and existing model artifacts
confirms prediction task, target metric, and baseline before any model training begins
prioritizes simple, production-proven models (logistic regression, gradient boosting) over complex architectures
schema checks, null handling, feature engineering, and train/test split automation
trains and evaluates multiple algorithms side-by-side with performance metrics
generates confusion matrices, ROC curves, feature importance, and error analysis
produces serialized models, prediction pipelines, and serving endpoint code
creates modular scripts (data_validation.py, train.py, evaluate.py, serve.py) for reproducibility
Example Output
Example 1: Classification Pipeline
✓ Detected: scikit-learn + pandas
✓ Task: Binary classification (churn prediction)
✓ Baseline metric: 72% accuracy (random)
✓ Model: Logistic Regression → 87% accuracy, F1: 0.84
✓ Alternative: XGBoost → 91% accuracy, F1: 0.89
✓ Deployment: FastAPI endpoint ready at localhost:8000/predict
Example 2: Regression Pipeline
✓ Detected: XGBoost + numpy
✓ Task: Price prediction (continuous output)
✓ Baseline metric: MAE $15,000 (mean estimate)
✓ Model: XGBoost → MAE $2,100, R²: 0.94
✓ Feature importance: square_footage (0.42), location (0.31), age (0.15)
✓ Serving code + model artifact generated
What's Included
- SKILL.md instruction file with ML pipeline workflow and environment detection logic:
- data_validation.py template: schema validation, null handling, feature engineering
- train.py template: baseline and advanced model training scripts
- evaluate.py template: metrics computation, confusion matrices, ROC curves, feature importance
- serve.py template: FastAPI/Flask endpoint code for model serving
- requirements.txt template: common ML dependencies (scikit-learn, XGBoost, TensorFlow, etc.)
Who It's For
- Data scientists building production ML pipelines without manual orchestration
- ML engineers automating model training and evaluation workflows
- Analytics engineers implementing predictive features in data platforms
- Backend engineers deploying machine learning models to serving endpoints
- Product teams rapidly prototyping classification and regression models
Best For
- Binary and multi-class classification tasks (churn, fraud, recommendation scoring)
- Regression models (price prediction, forecasting, resource estimation)
- Quick baseline establishment before hyperparameter tuning
- End-to-end pipeline generation from raw data to deployed model
- Model evaluation and diagnostics (feature importance, error analysis, metric comparison)
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