MLOps for eKYC

Definition

MLOps applies DevOps practices to machine learning — covering training pipelines, model versioning, experiment tracking, CI/CD for models, and production monitoring specific to eKYC.

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MLOps Pipeline

graph LR
    A[Data Collection<br/>& Labeling] --> B[Training Pipeline<br/>Experiment tracking]
    B --> C[Model Evaluation<br/>Cross-dataset testing]
    C --> D[Model Registry<br/>Versioning, metadata]
    D --> E[Deployment<br/>Canary, A/B, shadow]
    E --> F[Monitoring<br/>Drift, accuracy, latency]
    F -->|Drift detected| A

Key Tools

Category	Tools
Experiment tracking	MLflow, Weights & Biases, Neptune
Data versioning	DVC, LakeFS
Model registry	MLflow, AWS SageMaker, Vertex AI
Training orchestration	Kubeflow, Airflow, Metaflow
Feature store	Feast, Tecton
Monitoring	Evidently AI, WhyLabs, Arize
Deployment	Triton, SageMaker Endpoints, Vertex AI

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Key Takeaways

Summary

• MLOps is essential for eKYC — models must be continuously updated as attacks evolve
• Experiment tracking (W&B, MLflow) prevents losing track of model versions and results
• Shadow deployment (run new model alongside production without serving) validates before switching
• Drift monitoring triggers retraining before accuracy degrades in production

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Related Articles

• Model Drift & Retraining
• eKYC Monitoring & Observability