Evaluate your ML operations maturity and get a roadmap to excellence
Start Free Assessment →Data versioning, lineage tracking, quality monitoring, and feature store implementation
Experimentation tracking, model versioning, collaboration tools, and reproducibility
CI/CD pipelines, containerization, A/B testing, and automated rollback capabilities
Model performance tracking, drift detection, alerting, and business metric alignment
Pipeline automation, workflow orchestration, auto-retraining, and resource optimization
Model governance, compliance tracking, security controls, and audit trails
Manual, script-driven process. Data scientists work in silos.
Basic automation for deployment but ML-specific processes remain manual.
Automated model training with experiment tracking and model registry.
Full CI/CD for ML with automated testing and deployment.
End-to-end automation with continuous training and self-healing systems.
Faster model deployment
Reduction in model failures
Lower operational costs
More experiments per month
| Capability | Level 0 | Level 1 | Level 2 | Level 3 | Level 4 |
|---|---|---|---|---|---|
| Experiment Tracking | ✗ | ✗ | ✓ | ✓ | ✓ |
| Automated Training | ✗ | ✗ | ✓ | ✓ | ✓ |
| Model Registry | ✗ | ✗ | ✓ | ✓ | ✓ |
| Automated Deployment | ✗ | ✗ | ✗ | ✓ | ✓ |
| Model Monitoring | ✗ | ✗ | ✗ | ✓ | ✓ |
| Continuous Training | ✗ | ✗ | ✗ | ✗ | ✓ |
Complete MLOps maturity assessment to understand current state and gaps
Implement experiment tracking, version control, and basic automation
Build CI/CD pipelines, model registry, and monitoring infrastructure
Implement advanced features like auto-retraining and drift detection
Achieve full automation with self-optimizing ML systems
"The MLOps maturity assessment revealed we were at Level 1, spending 80% of our time on manual deployments. Following the roadmap, we reached Level 3 in 8 months. Now we deploy models 10x faster with 75% fewer production issues. The ROI has been incredible."