Organizations with mature DevOps practices often assume they can simply extend those practices to ML systems. Same CI/CD pipelines. Same testing approaches. Same team structures.
It doesn't work.
MLOps borrows from DevOps—versioning, automation, monitoring—but ML systems have a fundamental difference that changes everything: data.
The DevOps Model
DevOps is about code:
- Version control: Track changes to code
- Testing: Verify code does what it should
- CI/CD: Automate building and deploying code
- Monitoring: Watch code running in production
- Rollback: Revert to previous versions when things break
The system is deterministic. Same code, same inputs, same outputs. When something breaks, you can trace it to a specific code change and fix or revert it.
Testing is binary: it works or it doesn't. Either the button submits the form or it throws an error.
The MLOps Difference
ML systems aren't just code. They're code plus data plus model artifacts.
Data changes the equation:
- Models learn from data, not just execute instructions
- The same model code produces different behavior with different data
- Data changes over time even when code doesn't
- Quality is probabilistic, not binary
This means DevOps practices need significant adaptation.
Versioning Gets Harder
In DevOps, you version code.
In MLOps, you version:
- Code (training scripts, inference code, pipelines)
- Data (training datasets, validation datasets, feature definitions)
- Models (model artifacts, weights, configurations)
- Environments (dependencies, configurations, compute specs)
A production issue might be caused by any of these—or by the combination. Reproducing a specific training run requires capturing all of them.
Testing Becomes Probabilistic
DevOps testing: Does the function return the expected value?
MLOps testing: Does the model achieve acceptable performance across relevant scenarios?
You can't test ML systems with assertions like expect(result).toEqual(expected). Model outputs are probabilistic. The question isn't "is this correct?" but "is this good enough?"
Testing must cover:
- Accuracy metrics (precision, recall, F1, etc.)
- Fairness metrics (performance across demographic groups)
- Robustness (performance on edge cases, adversarial inputs)
- Drift sensitivity (behavior under distribution shift)
Deployment Isn't the End
DevOps deployment: Code ships, monitoring confirms it's running, job done (until the next release).
MLOps deployment: Model ships, degradation begins immediately.
Models don't stay good. They degrade over time as production data diverges from training data. A successful deployment is the start of ongoing monitoring, not the end of the workflow.
Rollback Is Complicated
DevOps rollback: Revert to previous code version. Everything works as it did before.
MLOps rollback: You can revert the model, but:
- Production data has changed since the old model was deployed
- The old model may perform worse on current data
- Retraining is often better than reverting
Rollback is a stop-gap, not a solution. ML systems need forward remediation strategies.
Organizational Implications
Different Skills Required
DevOps teams: Software engineers, SREs, infrastructure specialists.
MLOps teams: All of the above, plus data scientists, ML engineers, data engineers.
The skillset expands significantly. Teams need people who understand statistical testing, model behavior, and data quality—not just software systems.
Different Ownership Models
DevOps: Code ownership is clear. The team that wrote it maintains it.
MLOps: Who owns a model?
- Data scientists built it
- ML engineers deployed it
- Data engineers manage its inputs
- Platform engineers run the infrastructure
- Business stakeholders define success criteria
Ownership fragmentation causes problems. When models degrade, unclear ownership means slow response.
Different Feedback Loops
DevOps: Bugs are reported quickly. Users notice when features break.
MLOps: Degradation is silent. Models return predictions even when those predictions are wrong. By the time someone notices, the damage is done.
This requires proactive monitoring, not just reactive incident response. But monitoring alone isn't enough—you need AI supervision that can act on what monitoring reveals, enforcing constraints and triggering interventions before silent failures become visible damage.
What Stays the Same
MLOps does inherit valuable DevOps principles:
Automation: Manual processes don't scale. Automate training, testing, deployment, monitoring.
Version control: Everything should be versioned and reproducible.
CI/CD: Continuous integration and deployment—adapted for ML artifacts.
Infrastructure as code: Reproducible environments and deployments.
Monitoring and alerting: Observability into production systems.
Collaboration: Breaking down silos between development and operations.
The principles transfer. The implementation requires adaptation.
The Maturity Gap
Most organizations are further along in DevOps maturity than MLOps maturity:
- DevOps practices are decades old and well-understood
- Tooling is mature and widely available
- Best practices are established
- Talent is available
MLOps is younger:
- Practices are still evolving
- Tooling is maturing but fragmented
- Best practices are emerging but not standardized
- Talent is scarce
Organizations often underestimate this gap. They assume DevOps maturity will transfer, then struggle when ML systems require different approaches.
Building MLOps Capability
Start with Fundamentals
Before advanced automation:
- Establish model versioning
- Implement basic performance monitoring
- Create reproducible training pipelines
- Define ownership and accountability
Add Sophistication Gradually
- Drift detection and alerting
- Automated retraining triggers
- Fairness monitoring
- A/B testing infrastructure
- Feature stores and data quality monitoring
Learn from Incidents
When models fail:
- Conduct real post-mortems
- Identify process gaps
- Update practices based on learnings
MLOps isn't DevOps with different tools. It's a different discipline that requires different thinking.
Data changes the game. Probabilistic systems require probabilistic approaches. Continuous degradation requires continuous supervision—active oversight that goes beyond monitoring to enforce constraints and maintain control.
Organizations that recognize these differences build effective MLOps practices. Those that assume DevOps transfers directly keep wondering why their ML systems fail in production.
The data isn't going away. Adapt accordingly.
