The Four Pillars of MLOps
Reproducibility means that any training run can be exactly reproduced using the same data, the same code, and the same environment to produce the same model. Without reproducibility, debugging model failures is nearly impossible. Automation means removing manual steps from the model development and deployment process. Every step that a human must manually execute introduces delays, inconsistencies, and the possibility of error. Monitoring means continuously tracking the health of deployed models including their accuracy, input and output distributions, latency, and business impact, and alerting when something goes wrong. Governance means maintaining clear records of what data trained each model, what evaluation results it achieved, who approved it for deployment, and what changes have been made over time.
Version Control for Everything
DVC (Data Version Control) extends Git to handle large datasets and model files. It works alongside Git so code changes go in Git, data and model artifacts go in DVC-managed storage in S3 or GCS, and DVC files that track data versions go in Git. You can check out any historical version of your code and instantly retrieve the exact dataset that trained the corresponding model. Model versioning through a model registry like MLflow, Weights and Biases, or SageMaker Model Registry provides versioned catalogs of trained models with associated metadata including training data version, evaluation metrics, hyperparameters, code version, and deployment history.
Building the CI/CD Pipeline
The continuous integration pipeline should include code quality checks, unit tests for feature engineering functions, integration tests that verify the full training pipeline runs without errors on a small subset of data, and model evaluation tests that verify a newly trained model meets minimum quality thresholds. The continuous training pipeline retrieves the latest training data, runs the training code, evaluates the resulting model against validation data and the current production model, and either automatically deploys the new model if it passes all quality gates or flags it for human review.
Feature Stores: The Missing Link
A feature store is a centralized repository for engineered features that ensures consistency between features used during model training and features used during inference. The training-serving skew problem is a pervasive source of production ML failures. Features computed slightly differently during training and during inference can cause a model that performs well offline to perform poorly in production. Feast is the leading open-source feature store that manages a catalog of feature definitions, orchestrates computation of features from data sources, stores point-in-time correct feature values for training data creation, and provides a low-latency online serving layer typically backed by Redis.
Deployment Strategies
Blue-green deployment maintains two identical production environments. The new model is deployed to the green environment. After validation, traffic is switched from blue to green. If problems arise, traffic is switched back instantly. Canary deployment routes a small percentage of traffic to the new model while the remaining percentage continues to use the existing model. If the canary model performs well the percentage gradually increases. A/B testing deploys two or more model variants simultaneously to randomly assigned user segments, measuring business outcomes rather than just ML metrics to determine which model performs better in the context that matters.
