**Config-driven ML pipeline framework with DAG orchestration, model registry, and CI/CD templates for reproducible machine learning.**
Browse the actual product documentation and code examples included in this toolkit.
Key features of MLOps Pipeline Templates
• **DAG-based pipeline runner** that resolves stage dependencies, supports parallel branches, and retries failed stages with exponential backoff • **Feature engineering stage** with column transformers, target encoding, time-based feature extraction, and missing-value strategies • **Training stage** supporting scikit-learn, PyTorch, and TensorFlow with unified config • **Evaluation stage** that computes classification, regression, and ranking metrics with threshold gates • **Deployment stage** with blue/green, canary, and shadow deployment strategies • **Model registry** interface for versioning, stage transitions (staging → production → archived), and lineage tracking
**DAG-based pipeline runner** that resolves stage dependencies, supports parallel branches, and retries failed stages with exponential backoff
**Feature engineering stage** with column transformers, target encoding, time-based feature extraction, and missing-value strategies
**Training stage** supporting scikit-learn, PyTorch, and TensorFlow with unified config
**Evaluation stage** that computes classification, regression, and ranking metrics with threshold gates
**Deployment stage** with blue/green, canary, and shadow deployment strategies
**Model registry** interface for versioning, stage transitions (staging → production → archived), and lineage tracking
Configure MLOps Pipeline Templates parameters to see how the product works.
# 1. Install dependencies pip install -r requirements.txt # 2. Run the classification pipeline python -m src.pipeline_runner --config pipelines/classification_pipeline.yaml # 3. Check the output ls artifacts/ # Model files, metrics, reproducibility snapshots