MLOps & Model Operations
Don't just train models — operate them reliably and traceably.
Many AI models stay prototypes because the path to reliable production is missing. MLOps practices built on MLflow or similar tools let you version, monitor and update models systematically, so they stay performant, traceable and maintainable over time.
- More models make the leap from prototype to production.
- Early detection of model degradation.
- Traceable history for every model decision.
time to production
more frequent model updates
to live MLOps pipeline
undetected model drift
Calculated from the previous time from model training to stable production operation.
- 01
Maturity assessment
We assess existing models and the current operating process.
- 02
Pipeline design
We define training, validation and deployment steps.
- 03
Versioning & tracking
We set up experiment tracking and model registration.
- 04
Production monitoring
We continuously monitor model performance and data drift.
- 05
Retraining process
We establish a clear process for updates and rollback.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Training data
Historical, labeled datasets for model training.
Production data
Live data on which the model operates in production.
Model metrics
Accuracy and performance metrics per model version.
Feature definitions
Documented features and their calculation logic.
Feedback data
Feedback from the application on model quality.
Infrastructure metrics
Runtime and resource data from model serving.
Data science team
Less manual work in deployment and monitoring.
IT operations
Clear processes instead of ad hoc model deployments.
Business units
More reliable model results over time.
Executive management
A scalable path from pilot to company-wide AI use.
More models make the leap from prototype to production.
Early detection of model degradation.
Traceable history for every model decision.
Faster response to changing data conditions.
Less effort spent on manual model management.
With a solid data foundation this use case gets faster, cheaper and far more stable.
A central platform provides training data consistently.
Existing compute resources can be used flexibly for training.
Existing monitoring can be extended with model metrics.
Governance rules also apply to model access and outputs.
Lakehouse architecture
Training and production data are already consolidated.
Data quality & observability
Clean data reduces the risk of model drift.
Predictive maintenance
MLOps ensures stable, ongoing operation of predictive models.
Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.