Automated Retraining ML Pipelines – Keep Your ML Models Fresh

📚 Key Learnings

• Know the triggers for retraining (drift, performance, time, new data, business change).
• Design automated pipelines with version control and registries.
• Validate data/features before retraining; promote only better models.

🧠 Learn here

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What is ML Model Retraining?

Updating a model with new/additional data to maintain or improve performance over time (e.g., weekly recommendations reflecting seasonal trends and new products).

Why Retraining?

• Maintain accuracy as data patterns change
• Stay relevant to current distributions
• Adapt to new behaviors and use cases

Common Triggers

• Performance Drop: Metric decline vs baseline (e.g., accuracy < 85%).
• Data Drift: Input distribution shifts (EvidentlyAI, WhyLabs, Alibi Detect).
• Model Drift: Prediction behavior shifts (Alibi Detect, Arize AI).
• Time‑Based: Periodic schedule (e.g., weekly).
• New Data: Significant fresh labeled data.
• Business Change: New objectives/constraints.

What Happens During Retraining?

1. Collect new data (features + labels)
2. Preprocess and feature engineer (consistent with training)
3. Retrain model (reuse/tune architecture)
4. Evaluate & compare to production baseline
5. Deploy if it outperforms; rollback otherwise

Automation Tools

Automated Pipelines with Version Control

1. Data Ingestion: Batch/stream sources (Airflow, Kafka, Glue). Version inputs (DVC, LakeFS, Delta).
2. Data Validation: Great Expectations, TFDV for schema/quality checks.
3. Training: Containerized (Docker), scheduled or event‑based; track with MLflow/W&B.
4. Evaluation: Compare vs prod model on same validation set; use Evidently or custom scripts.
5. Model Registry: Register versions (MLflow/SageMaker); tag candidate/staging/prod; auto‑approve when better.
6. Deployment: Promote and deploy (Seldon, KServe, FastAPI) with monitoring (Prometheus/Grafana, Alibi).

Orchestration Choices

Feature & Data Validation

• Schema checks: columns/types present; mandatory fields
• Nulls and ranges: thresholds for missing/invalid values
• Statistical drift and outliers (Evidently, WhyLabs, Alibi, PyOD)
• Category/value domains; target balance

Model Registry Updates

Registries track versions, metadata, stages, and enable governance and rollback.

import mlflow
import mlflow.sklearn

with mlflow.start_run():
    mlflow.sklearn.log_model(model, "model")
    mlflow.log_metrics({"accuracy": 0.91})
    mlflow.set_tag("dataset_version", "v3.2")

    result = mlflow.register_model(
        "runs:/<run_id>/model", "ChurnPredictionModel"
    )
    client = mlflow.tracking.MlflowClient()
    client.transition_model_version_stage(
        name="ChurnPredictionModel",
        version=result.version,
        stage="Staging"
    )

• Register new version with artifacts/metrics/code hash.
• Promote from Staging → Production after tests.
• Notify stakeholders; trigger deployment pipelines.