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Search Experiment Tracking , found 23 results

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Cross Validation Setup

cross-validation-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

Data Augmentation Pipeline

data-augmentation-pipeline

jeremylongshore/claude-code-plugins-plus-skills

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Databricks MLflow Workflow

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Guides ML engineers through the Databricks secondary workflow that links Feature Store engineering, MLflow experiment tracking, model registry versioning, and serving so production-grade models can be trained and deployed.

Feature Engineering Helper

feature-engineering-helper

jeremylongshore/claude-code-plugins-plus-skills

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

Feature Importance Analyzer

feature-importance-analyzer

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Hyperparameter Tuner Assistant

hyperparameter-tuner

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

Mixed Precision Trainer

mixed-precision-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Production ML Pipeline

Jeffallan/claude-skills

Designs production-grade ML pipeline infrastructure covering experiment tracking, feature stores, DAG orchestration, and automated retraining so teams can reliably version data, tune hyperparameters, and deploy models with tools like MLflow, Kubeflow, Airflow, or Feast.

MLflow Lifecycle Tracker

Orchestra-Research/AI-Research-SKILLs

MLflow provides a framework-agnostic ML lifecycle toolkit for tracking experiments, logging models/artifacts, managing registries with versions/stages, and deploying across platforms so teams can reproduce and collaborate on PyTorch, TensorFlow, and Scikit-Learn workflows.

Mlflow Tracking Setup

mlflow-tracking-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for configuring mlflow experiment tracking, covering best practices, code generation, and validation to support ML training workflows.

Modern MLOps Engineering Guide

sickn33/antigravity-awesome-skills

A comprehensive MLOps engineer skill that covers building scalable ML infrastructure, pipelines, experiment tracking, registries, and cloud-native deployments across AWS, Azure, and GCP with best practices for reliable automation.

Optuna Study Creator

optuna-study-creator

jeremylongshore/claude-code-plugins-plus-skills

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.

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