Most teams don't have an MLOps problem; they have a sequencing problem. They know they need better deployment, testing, and monitoring, but they don't know which investment is premature for their current stage.
A common pitfall is over-engineering too early. As we've discussed before, sometimes you need to stop building platforms and start shipping features. This MLOps maturity model helps you identify exactly where you are and what your next strategic move should be.
The 5 Levels of MLOps Maturity
| Level | Focus | Primary Tools | Next Step |
|---|---|---|---|
| 1. Manual | Experimentation | Jupyter, Local Python | Scripting & Versioning |
| 2. Repeatable | Packaging | Docker, Git, MLflow | CI/CD & Basic Monitoring |
| 3. Operable | Automation | Argo CD, GitHub Actions | Governance & Scale |
| 4. Governed | Multi-team leverage | Kubeflow, Feature Stores | Self-service & Policy-as-Code |
| 5. Adaptive | Optimization | KEDA, Automated Retraining | Unit Economics & Efficiency |
Level 3: The "Operable" Pipeline
At Level 3, your team has moved beyond "it works on my machine" to a standardized MLOps pipeline on Kubernetes. This stage is characterized by declarative deployments where your model's state is stored in Git.
Technical Implementation: GitOps with Argo CD
For a Level 3 team, a model deployment shouldn't be a manual kubectl apply. It should be a managed resource.
# Example: Argo CD Application for a Model Service
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: fraud-detection-v2
spec:
project: default
source:
repoURL: 'https://github.com/resiliotech/ml-deployments.git'
targetRevision: HEAD
path: charts/model-service
destination:
server: 'https://kubernetes.default.svc'
namespace: ml-production
syncPolicy:
automated:
prune: true
selfHeal: true
Level 4 & 5: When to Build an Internal Platform
Reaching Level 4 usually involves the difficult decision of building an internal AI team vs. hiring consultants. At this stage, the platform itself becomes a product. You are no longer just deploying models; you are providing a "Golden Path" for other data scientists to deploy safely.
Final Takeaway
The most useful maturity model is the one that tells you what to stop doing as much as what to start doing. Don't build for Level 5 if you haven't mastered Level 2 reproducibility.
Resilio Tech specializes in helping teams navigate this maturity curve. Whether you are a Level 1 startup trying to get your first model into production or a Level 4 enterprise looking to automate governance and cost, we provide the architectural guidance and implementation expertise to get you to the next level without the waste.
Where does your team sit on the maturity curve? Contact Resilio Tech for a comprehensive MLOps audit and custom infrastructure roadmap.
