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Found 35 Skills
You are an **AI Engineer**, an expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. You focus on building intelligent featu...
Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model". Trigger with relevant phrases based on skill purpose.
Build a production-ready multilabel classifier on tabular data using XGBoost wrapped in MultiOutputClassifier. Use when each row can have multiple labels simultaneously (tags, attributes, gene functions, content moderation categories, multi-disease detection). Covers hamming loss, per-label metrics, label co-occurrence, MultiOutputClassifier vs ClassifierChain, and per-label SHAP. Default to this for any tabular multilabel problem.
Roc Curve Plotter - Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category.
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow ARN (arn:aws:sagemaker:...:mlflow-app/... or arn:aws:sagemaker:...:mlflow-tracking-server/...) or "SageMaker Managed MLflow".
Build a production-ready regression model on tabular data using XGBoost with conformalized quantile regression for prediction intervals. Use when the user needs to predict a continuous target from tabular features (price, sales, demand, time-to-event, score) and report uncertainty alongside the point estimate. Default to this for any tabular regression task.
Confusion Matrix Generator - Auto-activating skill for ML Training. Triggers on: confusion matrix generator, confusion matrix generator Part of the ML Training skill category.
Catalyst Zia Services and QuickML — OCR, Face Analytics, Text Analytics, Object Detection, Barcode Reader, Content Moderation, and AutoML predictions. Trigger on 'Zia', 'QuickML', 'OCR', 'face detection', 'text analytics', 'AutoML', 'ML model', or 'train a model on Catalyst'. DC restrictions: Identity Scanner is IN DC only; AutoML/QuickML is not available in EU, AU, IN, JP, SA, CA data centers.
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, convergence warnings from optimizers, cross-validation failures due to class imbalance, data leakage causing suspiciously high scores, or preprocessing errors with ColumnTransformer and feature alignment.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.