Editing AI Infrastructure and MLOps (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ MLOps Maturity Levels
! Level !! Description !! Automation !! Typical Organization
|-
| Level 0 || Manual process: scripts, Jupyter notebooks || None || Startups, research teams
|-
| Level 1 || ML pipelines automated; manual deployment || Data pipeline CT || Small ML teams
|-
| Level 2 || Full CI/CD for ML; automated retraining || Full CT + CD || Mature ML teams
|-
| Level 3 || Continuous monitoring, auto-retraining, auto-deployment || Fully automated || Large-scale ML platforms
|}

'''Common failures and anti-patterns:'''
* '''Training-serving skew''' — Features computed differently at training time vs. inference time. Example: training used the full historical average; inference uses a 30-day rolling average. Results in silent model degradation. A feature store solves this.
* '''No monitoring''' — Model is deployed and forgotten. Performance degrades silently as data distribution shifts. Always implement prediction monitoring on day one.
* '''Irreproducible experiments''' — Data and code versions not tracked; can't reproduce the best model from 3 months ago. Use data versioning (DVC, Delta Lake) and code pinning from the start.
* '''Model versioning chaos''' — Multiple model versions in production with no tracking of which version serves which traffic. Model registry + blue/green deployment is the solution.
* '''GPU waste''' — Training jobs that reserve GPUs but run at 10% utilization due to data loading bottlenecks. Profile GPU utilization; use multiple data loader workers, DALI for GPU-accelerated preprocessing.
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