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MLOps Tutorial: CI/CD, Feature Stores, Monitoring, Drift Detection (2026)

Sandip Mhaske • Ayodhyya • machine-learning

MLOps Tutorial: CI/CD, Feature Stores, Monitoring, Drift Detection (2026)

Published:  |  Category: Machine Learning  |  Reading time: ~15 min
MLOps Tutorial: CI/CD, Feature Stores, Monitoring, Drift Detection (2026)

MLOps brings DevOps principles to machine learning, ensuring reliable and scalable ML systems. It covers CI/CD pipelines, feature stores, model monitoring, and drift detection.

By 2026, MLOps is essential for any organization deploying ML in production. The tools and practices have matured, making enterprise-grade ML operations accessible to teams of all sizes.

📖 Table of Contents
  1. ML CI/CD Pipelines
  2. Feature Stores
  3. Model Monitoring and Observability
  4. Drift Detection Methods
  5. A/B Testing and Canary Deployments
  6. Reproducibility and Governance

ML CI/CD Pipelines

CI/CD pipelines automate training, testing, and deployment. GitHub Actions, GitLab CI, and Jenkins trigger on code or data changes. Stages include data validation, training, evaluation, model packaging, and deployment.

Our pipeline reduced deployment time from days to 30 minutes. Automated testing catches data quality issues and performance regressions before they reach production.

# CI/CD stages\n# 1. Data validation: Great Expectations\n# 2. Training: python train.py --config config.yaml\n# 3. Evaluation: python evaluate.py --model model.pkl\n# 4. Integration tests: pytest tests/\n# 5. Build Docker image\n# 6. Deploy canary, then full rollout

Feature Stores

Feature stores centralize feature engineering, storage, and serving. Feast and Tecton provide online and offline serving with point-in-time correctness.

I migrated our team from scattered feature scripts to Feast. Feature reuse across models increased by 60 percent and training-serving skew was eliminated.

from feast import FeatureStore\nstore = FeatureStore(repo_path="./feature_repo")\n# Get training features\ntraining_df = store.get_historical_features(entity_df=entity_df, features=["driver_stats:conv_rate"]).to_df()\n# Get online features for serving\nfeatures = store.get_online_features(features=["driver_stats:conv_rate"], entity_rows=[{"driver_id": 1001}])

Model Monitoring and Observability

Monitor prediction distributions, feature drift, and performance metrics. Use Prometheus for metrics, Grafana for dashboards, and custom alerting for anomalies.

Our monitoring dashboard tracks latency percentiles, prediction volume, feature distributions, and data drift scores. Alerts page on-call engineers when drift exceeds thresholds.

# Prometheus metrics\nfrom prometheus_client import Histogram, Counter\nprediction_latency = Histogram("prediction_latency_seconds", "Prediction latency")\nprediction_counter = Counter("predictions_total", "Total predictions")\n@prediction_latency.time()\nasync def predict(req):\n    prediction_counter.inc()\n    return model.predict(req.features)

Drift Detection Methods

Data drift detects changes in feature distributions using statistical tests like KS-test, PSI, and Wasserstein distance. Concept drift detects changes in the relationship between features and target.

I use the Alibi-Detect library for drift detection. The KS-test detector flags drifted features, and the classifier-based detector identifies concept drift with configurable threshold.

from alibi_detect.cd import KSDrift\nfrom alibi_detect.utils.saving import save_detector, load_detector\ncd = KSDrift(p_val=0.05, X_ref=reference_data)\npreds = cd.predict(current_data)\nif preds["data"]["is_drift"]:\n    print(f"Drift detected: {preds["data"]["threshold"]}")

A/B Testing and Canary Deployments

Canary deployments route a small percentage of traffic to the new model, gradually increasing if metrics are healthy. A/B testing compares model versions on live traffic.

Our canary process: 5 percent traffic for 1 hour, check metrics, increase to 25 percent, then 100 percent. Automated rollback if any metric degrades by more than 5 percent.

# Canary deployment logic\ndef deploy_canary(new_model, initial_traffic=0.05):\n    while current_traffic < 1.0:\n        route_traffic(new_model, current_traffic)\n        if check_metrics_degraded():\n            rollback()\n            return False\n        current_traffic = min(current_traffic * 2, 1.0)\n    promote_to_production(new_model)\n    return True

Reproducibility and Governance

Reproducibility requires tracking code, data, model, and environment. MLflow and DVC provide lineage tracking. Model governance ensures compliance with regulations.

For a healthcare client, we tracked every model version with its training data hash, hyperparameters, and evaluation metrics. Audit reports export the complete lineage in minutes.

# Track everything with MLflow\nwith mlflow.start_run() as run:\n    mlflow.log_params(params)\n    mlflow.log_artifact("data/processed")\n    mlflow.pytorch.log_model(model, "model")\n    mlflow.log_metrics(metrics)

Frequently Asked Questions

What is the difference between MLOps and DevOps?

MLOps extends DevOps with ML-specific concerns: data versioning, model evaluation, feature stores, drift detection, and model governance.

Do I need a feature store?

For teams with multiple models sharing features, a feature store prevents duplicate engineering and ensures training-serving consistency. Start simple, add when needed.

How often should models be retrained?

Depends on data drift velocity. Monitor performance and drift metrics. Retrain when drift is detected or performance drops below threshold. Weekly retraining is common.

What is model governance?

Process for managing model versions, approvals, and audit trails. Required in regulated industries. MLflow Model Registry and W&B Model Registry provide governance features.

Originally published on Ayodhyyya. Last updated June 1, 2026.