13 skills found
データ準備からモデル学習、検証、本番デプロイメントまでのエンドツーエンドMLOpsパイプラインを構築します。MLパイプラインの作成、MLOpsプラクティスの実装、またはモデル学習とデプロイメントワークフローの自動化時に使用します。
Wandb Experiment Logger - Auto-activating skill for ML Training.Triggers on: wandb experiment logger, wandb experiment loggerPart of the ML Training skill category.
Sklearn Pipeline Builder - Auto-activating skill for ML Training.Triggers on: sklearn pipeline builder, sklearn pipeline builderPart of the ML Training skill category.
Confusion Matrix Generator - Auto-activating skill for ML Training.Triggers on: confusion matrix generator, confusion matrix generatorPart of the ML Training skill category.
Feature Engineering Helper - Auto-activating skill for ML Training.Triggers on: feature engineering helper, feature engineering helperPart of the ML Training skill category.
Dataset Loader Creator - Auto-activating skill for ML Training.Triggers on: dataset loader creator, dataset loader creatorPart of the ML Training skill category.
Pytorch Model Trainer - Auto-activating skill for ML Training.Triggers on: pytorch model trainer, pytorch model trainerPart of the ML Training skill category.
Tensorflow Model Trainer - Auto-activating skill for ML Training.Triggers on: tensorflow model trainer, tensorflow model trainerPart of the ML Training skill category.
Learning Rate Scheduler - Auto-activating skill for ML Training.Triggers on: learning rate scheduler, learning rate schedulerPart of the ML Training skill category.
Train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to "train model" or "evalua... Trigger with relevant phrases based on skill purpose.
Split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
