azure-ml-deployer
Azure Ml Deployer - Auto-activating skill for ML Deployment. Triggers on: azure ml deployer, azure ml deployer Part of the ML Deployment skill category.
Azure Ml Deployer - Auto-activating skill for ML Deployment. Triggers on: azure ml deployer, azure ml deployer Part of the ML Deployment skill category.
This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.
This skill enables Claude to validate the ethical implications and fairness of AI/ML models and datasets. It is triggered when the user requests an ethics review, fairness assessment, or bias detection for an AI system. The skill uses the ai-ethics-validator plugin to analyze models, datasets, and code for potential biases and ethical concerns. It provides reports and recommendations for mitigating identified issues, ensuring responsible AI development and deployment. Use this skill when the user mentions "ethics validation", "fairness assessment", "bias detection", "responsible AI", or related terms in the context of AI/ML.
Model Pruning Helper - Auto-activating skill for ML Deployment. Triggers on: model pruning helper, model pruning helper Part of the ML Deployment skill category.
This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.
This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."
This skill empowers Claude to build AutoML pipelines using the automl-pipeline-builder plugin. It is triggered when the user requests the creation of an automated machine learning pipeline, specifies the use of AutoML techniques, or asks for assistance in automating the machine learning model building process. The skill analyzes the context, generates code for the ML task, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when the user explicitly asks to "build automl pipeline", "create automated ml pipeline", or needs help with "automating machine learning workflows".
Learning Rate Scheduler - Auto-activating skill for ML Training. Triggers on: learning rate scheduler, learning rate scheduler Part of the ML Training skill category.
This skill enables Claude to track and manage AI/ML model versions using the model-versioning-tracker plugin. It should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control for AI/ML models. Use this skill when the user mentions "track versions", "model registry", "MLflow", or requests assistance with AI/ML model deployment and management. This skill facilitates the implementation of best practices for model versioning, automation of model workflows, and performance optimization.
Hyperparameter Tuner - Auto-activating skill for ML Training. Triggers on: hyperparameter tuner, hyperparameter tuner Part of the ML Training skill category.
This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.
This skill enables Claude to provide interpretability and explainability for machine learning models. It is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior. The skill leverages techniques like SHAP and LIME to generate explanations. It is useful when debugging model performance, ensuring fairness, or communicating model insights to stakeholders. Use this skill when the user mentions "explain model", "interpret model", "feature importance", "SHAP values", or "LIME explanations".
Onnx Converter - Auto-activating skill for ML Deployment. Triggers on: onnx converter, onnx converter Part of the ML Deployment skill category.
Optimize LangChain application performance and latency. Use when reducing response times, optimizing throughput, or improving the efficiency of LangChain pipelines. Trigger with phrases like "langchain performance", "langchain optimization", "langchain latency", "langchain slow", "speed up langchain".
Optuna Study Creator - Auto-activating skill for ML Training. Triggers on: optuna study creator, optuna study creator Part of the ML Training skill category.
This skill enables Claude to optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. It is used when the user requests hyperparameter tuning, model optimization, or improvement of model performance. The skill analyzes the current context, generates code for the specified search strategy, handles data validation and errors, and provides performance metrics. Trigger terms include "tune hyperparameters," "optimize model," "grid search," "random search," and "Bayesian optimization."
Triton Inference Config - Auto-activating skill for ML Deployment. Triggers on: triton inference config, triton inference config Part of the ML Deployment skill category.
Model Quantization Tool - Auto-activating skill for ML Deployment. Triggers on: model quantization tool, model quantization tool Part of the ML Deployment skill category.
Sklearn Pipeline Builder - Auto-activating skill for ML Training. Triggers on: sklearn pipeline builder, sklearn pipeline builder Part of the ML Training skill category.
Model Drift Detector - Auto-activating skill for ML Deployment. Triggers on: model drift detector, model drift detector Part of the ML Deployment skill category.
This skill trains machine learning models using automated workflows. It analyzes datasets, selects appropriate model types (classification, regression, etc.), configures training parameters, trains the model with cross-validation, generates performance metrics, and saves the trained model artifact. Use this skill when the user requests to "train" a model, needs to evaluate a dataset for machine learning purposes, or wants to optimize model performance. The skill supports common frameworks like scikit-learn.
Torchscript Exporter - Auto-activating skill for ML Deployment. Triggers on: torchscript exporter, torchscript exporter Part of the ML Deployment skill category.
This skill enables Claude to validate the ethical implications and fairness of AI/ML models and datasets. It is triggered when the user requests an ethics review, fairness assessment, or bias detection for an AI system. The skill uses the ai-ethics-validator plugin to analyze models, datasets, and code for potential biases and ethical concerns. It provides reports and recommendations for mitigating identified issues, ensuring responsible AI development and deployment. Use this skill when the user mentions "ethics validation", "fairness assessment", "bias detection", "responsible AI", or related terms in the context of AI/ML.
Configure fastapi ml endpoint operations. Auto-activating skill for ML Deployment. Triggers on: fastapi ml endpoint, fastapi ml endpoint Part of the ML Deployment skill category. Use when working with APIs or building integrations. Trigger with phrases like "fastapi ml endpoint", "fastapi endpoint", "fastapi".