MLOps vs DevOps: Understanding the Differences

MLOps applies DevOps principles to machine learning—but ML has unique challenges that standard DevOps practices don't address. This guide explains the relationship, key differences, and metrics that matter for ML systems.

"MLOps is DevOps plus data versioning, model tracking, and drift detection. The code is only half the system."

What Is MLOps?

MLOps (Machine Learning Operations) extends DevOps practices to machine learning systems. The core difference: ML systems depend on both code AND data. Changes to either can break production.

Why ML Needs Special Treatment

Data dependency: Model behavior depends on training data, not just code
Model drift: Models degrade over time as real-world data changes
Reproducibility: Same code + different data = different model
Experimentation: ML development involves many failed experiments
Explainability: Need to understand why models make decisions

MLOps vs. DevOps Comparison

Aspect	DevOps	MLOps
Primary Artifact	Code (versioned in Git)	Code + Data + Model
Testing	Unit, integration, E2E	+ Data validation, model validation
CI/CD Pipeline	Build → Test → Deploy	+ Train → Evaluate → Register
Monitoring	Latency, errors, uptime	+ Model drift, data drift, prediction quality
Rollback	Deploy previous code version	Deploy previous model + may need retraining

DevOps vs MLOps pipeline comparison showing the additional stages required for machine learning systems including data collection, training, and drift monitoring — MLOps extends DevOps with data pipelines, model training, and continuous retraining loops

Detect code hotspots and knowledge silos with CodePulse

MLOps-Specific Metrics

Model Quality Metrics

Metric	Definition	Why It Matters
Model Accuracy	Prediction correctness on holdout data	Core quality measure
Model Drift	Accuracy degradation over time	Triggers retraining
Data Drift	Input distribution change	Early warning of model issues
Prediction Latency	Time from request to prediction	User experience

Operational Metrics

Metric	Definition	Target
Training Time	Time to train a model	Depends on model size
Model Deployment Frequency	How often models are updated	Varies by use case
Experiment Success Rate	% of experiments that improve metrics	>20% (ML is experimental)
Time to Production	Experiment to deployed model	Days to weeks (not months)

/// Our Take

Most teams don't need MLOps—they need DevOps for their ML code first.

If your ML team deploys manually and doesn't have CI/CD, starting with "MLOps platforms" is premature. Get basic DevOps working (version control, automated testing, CI/CD), then layer on ML-specific tools (experiment tracking, model registry, drift monitoring).

MLOps Tools Landscape

Category	Tools	Purpose
Experiment Tracking	MLflow, Weights & Biases, Neptune	Track experiments, compare results
Feature Stores	Feast, Tecton, Databricks Feature Store	Manage and serve features
Model Registry	MLflow, SageMaker, Vertex AI	Version and stage models
Orchestration	Kubeflow, Airflow, Dagster	Pipeline automation
Monitoring	Evidently, Arize, WhyLabs	Drift detection, model quality

📊 How CodePulse Fits

CodePulse tracks the software engineering side of ML development:

Code velocity: PR cycle time for ML code changes
Collaboration: Code review patterns for ML repos
Delivery: How often ML code ships (distinct from model deployment)

For model-specific metrics (drift, accuracy), use dedicated MLOps monitoring tools. For engineering metrics, use CodePulse.

When to Invest in MLOps

You Need MLOps When:

Multiple models in production
Models need frequent retraining
Data scientists spending >50% time on operations
Model quality issues in production
Compliance/audit requirements for ML

You Don't Need MLOps (Yet) When:

One or two models, updated rarely
Still proving ML value to the business
Basic DevOps isn't working yet
Small team (<3 ML practitioners)

DevOps Automation Metrics Guide — Foundation for MLOps
DORA Metrics Guide — Delivery metrics apply to ML too
DevOps Toolchain Guide — Understanding the full toolchain

Conclusion

MLOps extends DevOps to handle the unique challenges of machine learning: data versioning, model tracking, drift detection, and experiment management. But the foundation is still good DevOps—version control, CI/CD, monitoring, and automation.

"In ML, the code is reproducible but the model isn't—unless you track the data and parameters too."

Get DevOps fundamentals right first. Layer on MLOps tools as ML maturity grows. Track your ML engineering metrics with CodePulse while using dedicated MLOps tools for model-specific monitoring.