Our organization is scaling predictive analytics in Qlik Sense cloud and we’re struggling with model governance. We need to establish model versioning strategy for tracking changes, implement validation framework for model quality, create deployment approval workflow for production releases, and set up performance monitoring for deployed models. How are others handling MLOps-style governance for predictive models in Qlik? What frameworks ensure models are properly validated and approved before deployment while maintaining agility?
Here’s our comprehensive model governance framework for predictive analytics in Qlik Sense cloud:
Model Versioning Strategy:
We implemented a multi-layer versioning approach:
-
Code Versioning: All model development code (Python scripts, R scripts, Qlik load scripts) stored in Git with semantic versioning (major.minor.patch)
- Major version: Fundamental model architecture changes
- Minor version: Feature engineering changes or hyperparameter updates
- Patch version: Bug fixes or minor tweaks
-
Model Artifact Versioning: Trained model files stored in cloud object storage with metadata:
- Model ID (unique identifier)
- Version number (linked to code version)
- Training date and data version
- Performance metrics from validation
- Training parameters and hyperparameters
-
Data Versioning: Training and validation datasets versioned using DVC (Data Version Control)
- Enables reproducibility of model training
- Tracks data lineage and transformations
- Critical for understanding model behavior over time
-
Deployment Versioning: Track which model versions are deployed in each environment (dev/staging/prod)
- Enables quick rollback if issues arise
- Provides audit trail of production changes
Integration with Qlik: We use Qlik’s extension framework to load versioned models and track which version is active in each app.
Validation Framework:
Before any model can be promoted to production, it must pass our validation framework:
Technical Validation (Automated):
- Minimum accuracy threshold (varies by use case, typically 80%+)
- Cross-validation performance (K-fold validation, minimum 5 folds)
- Out-of-sample testing on holdout dataset
- Performance comparison against baseline model (must show improvement)
- Prediction latency testing (must complete within SLA)
- Robustness testing with edge cases and outliers
Data Quality Validation (Automated):
- Data drift detection comparing training data to recent production data
- Feature distribution analysis
- Missing value handling verification
- Outlier detection and handling
- Data schema validation
Business Validation (Manual Review):
- Model predictions align with business logic and domain expertise
- Edge case behavior is acceptable
- Model limitations are documented and understood
- Business stakeholder approval
Ethical Validation (Manual Review):
- Bias testing across demographic groups
- Fairness metrics evaluation
- Explainability requirements met (SHAP values, feature importance)
- Privacy and data usage compliance verified
All validation results stored in model registry with pass/fail status for each criterion.
Deployment Approval Workflow:
We implemented a four-stage deployment pipeline:
Stage 1: Development
- Data scientists develop and train models
- Automated validation runs on every commit
- Models deployed to personal development environments
- No approval required
Stage 2: Staging/Validation
- Models passing automated validation promoted to staging
- Peer review required (another data scientist reviews code and methodology)
- Business stakeholder review of model predictions
- Approval required from: Lead Data Scientist
Stage 3: Pre-Production Testing
- Shadow deployment: model runs alongside production model but predictions aren’t used
- Performance monitoring for 1-2 weeks
- Comparison of new model vs. existing production model
- Approval required from: Platform Manager + Business Owner
Stage 4: Production Deployment
- Final approval meeting with governance council
- Documentation review (model card, technical specs, limitations)
- Deployment plan review (rollback procedures, monitoring plan)
- Approval required from: Governance Council (includes compliance, IT, business, data science)
- Phased rollout: 10% traffic → 50% traffic → 100% traffic over 2 weeks
Separation of duties enforced: model developers cannot approve their own models beyond development stage.
Performance Monitoring:
Continuous monitoring of deployed models:
Real-Time Monitoring:
- Prediction latency (alert if >2x baseline)
- Prediction volume (alert on unexpected spikes or drops)
- Error rates (alert on any errors)
- API availability (alert if service down)
Daily Monitoring:
- Prediction accuracy on labeled data (when available)
- Feature distribution drift detection
- Model confidence scores distribution
- Data quality metrics
Weekly Monitoring:
- Business KPI impact analysis
- A/B testing results (new model vs. baseline)
- User feedback and reported issues
- Fairness metrics across demographic groups
Monthly Monitoring:
- Comprehensive model performance review
- Cost-benefit analysis
- Retraining decision (based on performance degradation)
- Governance compliance audit
Automated Alerting:
- Prediction accuracy drops >5% from baseline
- Data drift score exceeds threshold
- Feature values outside expected ranges
- Prediction latency exceeds SLA
Alerts trigger automated workflows: notify model owner, create investigation ticket, potentially trigger automatic rollback for severe issues.
Qlik-Specific Implementation:
In Qlik Sense environment:
- Models deployed as Python/R extensions or via API calls to model serving layer
- Model metadata stored in Qlik apps for transparency
- Monitoring dashboards built in Qlik showing model performance metrics
- Version control integrated with app deployment process
Governance Council Structure:
Quarterly meetings with:
- Chief Data Officer (chair)
- Lead Data Scientists (2-3 representatives)
- IT/Platform Management
- Compliance Officer
- Business Unit Representatives
- Ethics/Risk Management
Responsibilities:
- Review and approve high-risk model deployments
- Set governance policies and standards
- Review model performance trends
- Address escalated issues
- Approve exceptions to governance policies
Results After Implementation:
- 95% of models pass validation on first attempt (up from 60%)
- Average time from development to production: 3 weeks (down from 8 weeks)
- Zero production incidents due to model failures
- Complete audit trail for all deployed models
- Model performance degradation detected and addressed proactively
- Increased confidence from business stakeholders and compliance team
The framework provides necessary governance rigor while maintaining deployment agility through automation and clear processes.
From a compliance perspective, model governance is essential especially for regulated industries. We require full audit trails showing who developed models, what data was used, validation results, and approval history. The deployment workflow must enforce separation of duties - developers can’t approve their own models for production. Documentation requirements include model cards describing purpose, limitations, and ethical considerations.
I’d recommend looking at MLOps best practices from the broader data science community and adapting them for Qlik. Tools like MLflow, DVC, or Kubeflow provide model registry, versioning, and deployment capabilities. You can integrate these with Qlik Sense through APIs. The challenge is that Qlik’s predictive analytics capabilities are somewhat limited compared to full ML platforms, so you need to decide what level of sophistication you need.
Raj, can you elaborate on how you integrated Git with Qlik Sense for model versioning? Also, what validation criteria do you use before promoting models from development to production? We’re concerned about models degrading over time without proper monitoring.