II.
LibraryProcess overview
Reference · livelib-process:data-science-ml--model-training-pipeline
model-training-pipeline overview
Model Training Pipeline with Experiment Tracking - Execute model training with hyperparameter tuning, track experiments with metrics and artifacts, compare model variants, and select best performers with automated quality gates and convergence criteria.
Attributes
displayName
model-training-pipeline
description
Model Training Pipeline with Experiment Tracking - Execute model training with hyperparameter tuning,
track experiments with metrics and artifacts, compare model variants, and select best performers with automated
quality gates and convergence criteria.
libraryPath
library/specializations/data-science-ml/model-training-pipeline.js
specialization
data-science-ml
references
- - MLflow Experiment Tracking: https://mlflow.org/ - Weights & Biases: https://wandb.ai/ - Kubeflow Pipelines: https://www.kubeflow.org/ - TensorFlow: https://www.tensorflow.org/ - PyTorch: https://pytorch.org/ - Scikit-learn Model Selection: https://scikit-learn.org/stable/model_selection.html - Optuna Hyperparameter Optimization: https://optuna.org/
example
const result = await orchestrate('specializations/data-science-ml/model-training-pipeline', {
projectName: 'Churn Prediction Model',
modelType: 'classification',
trainingData: 'data/train.csv',
validationData: 'data/val.csv',
targetMetric: 'f1_score',
targetPerformance: 0.85,
maxIterations: 10,
hyperparameterStrategy: 'bayesian'
});
usesAgents
- general-purpose
Outgoing edges
lib_applies_to_domain1
- domain:data-science·DomainData Science
lib_belongs_to_specialization1
- specialization:data-science-ml·Specialization
lib_implements_workflow2
- workflow:release-management·Workflow
- workflow:data-pipeline-deployment·WorkflowData Pipeline Deployment
Incoming edges
None.