Machine learning systems are fundamentally different from traditional software: they are probabilistic, data-dependent, and require continuous monitoring and retraining. MLOps adapts DevOps principles to address these unique challenges, providing the practices and tools needed to deploy, monitor, and maintain ML systems reliably in production. In 2026, MLOps maturity is a key differentiator for organizations deriving value from AI investments.

Version Control for Everything

Traditional version control tracks code, but ML systems depend equally on data and model artifacts. Data versioning tools like DVC track dataset changes alongside code. Model registries store trained models with metadata about training data, hyperparameters, and evaluation metrics. Experiment tracking platforms like MLflow or Weights and Biases log every training run for reproducibility. The combination of code, data, and model versioning ensures that any production model can be traced back to exactly the code, data, and configuration that produced it.

CI/CD for Machine Learning

ML pipelines require specialized CI/CD that goes beyond code testing. Automated quality gates validate model performance against baseline metrics, fairness criteria, and latency requirements. Data validation checks ensure training data meets quality standards. Feature store consistency verifies that training and serving features are computed identically. Integration tests verify that the complete pipeline from data ingestion through model serving works correctly. These automated checks catch issues before they reach production.

Monitoring and Observability

ML monitoring goes beyond traditional application monitoring. Data drift detection monitors input feature distributions for changes that could degrade model performance. Prediction monitoring tracks output distributions for anomalies. Performance metrics track accuracy, latency, and throughput against baseline expectations. Business metrics connect model performance to business outcomes. When monitoring detects degradation, automated retraining pipelines can update models without manual intervention.

Feature Stores

Feature stores provide centralized, consistent feature computation for training and serving. They solve the training-serving skew problem where features computed differently during training and serving produce inconsistent results. Feature stores provide versioned, documented feature definitions that ensure consistency across teams and projects. Online feature stores provide low-latency feature access for real-time inference, while offline stores support batch training.

Model Governance and Compliance

As ML models make increasingly consequential decisions, governance becomes essential. Model cards document model capabilities, limitations, and intended use cases. Audit trails record every model change, including who approved it and what evaluation results supported the decision. Compliance frameworks ensure models meet regulatory requirements for fairness, explainability, and accuracy. These governance practices build trust and enable responsible AI deployment at scale.