ML Pipeline Maturity Assessment
Evaluate your MLOps maturity across experiment tracking, feature stores, model registry, ML CI/CD, and production monitoring. Based on Google's MLOps maturity framework.
- Free Assessment
- MLOps Best Practices
- Google MLOps Levels
ML Pipeline Maturity Assessment
Frequently asked questions
What are the Google MLOps maturity levels?
Level 0: Manual ML process. Level 1: ML pipeline automation (automated training, validation, deployment). Level 2: CI/CD pipeline automation (automated testing, continuous training triggers, A/B testing in production).
Do I need a feature store?
If you have more than 2-3 ML models sharing features, or if training-serving skew is a concern, a feature store pays for itself. It ensures consistency between training and serving, and prevents duplicate feature engineering.
What is model drift?
Model drift occurs when a model's predictions degrade over time because the underlying data distribution changes. It includes data drift (input changes), concept drift (relationship changes), and prediction drift (output distribution changes).
How often should models be retrained?
It depends on data velocity. E-commerce recommendation models may need weekly retraining, while fraud detection models might retrain daily. Set up drift monitors to trigger retraining based on performance degradation.
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