Machine Learning Engineering
From data architecture to production-ready models.
Technical Discovery Call
Ergebnisse im Detail
Defines the data landscape, quality standards, and integration paths as the basis for all model development.
Enthält- Data source mapping & quality analysis
- Schema design & feature store architecture
- Integration interfaces & ETL pipelines
Delivers tested, optimized models with documented training history and validation results.
Enthält- Model artifacts with version history
- Validation reports (cross-val, out-of-sample)
- Hyperparameter documentation & reproducibility
Makes model logic transparent — for business units, management, and regulatory requirements.
Enthält- Feature importance & SHAP analysis
- Bias screening & fairness metrics
- Explanation views for different audiences
Enables productive use of models through standardized, documented interfaces.
Enthält- REST/GraphQL API specification
- Authentication & rate limiting
- SDK documentation & example code
Ensures models remain stable in production, drift is detected, and retraining cycles are defined.
Enthält- CI/CD pipeline for model deployments
- Drift detection & performance monitoring
- Alerting & retraining strategy
Unser Vorgehen
- Existing data sources & systems
- Business requirements & use cases
- Compliance framework conditions
- Data architecture documentation as a working basis
- Prepared data pipelines
- Defined target metrics & KPIs
- Baseline models or benchmarks
- Trained models + validation reports
- Trained models from Phase 2
- Validation data & test scenarios
- Regulatory requirements (XAI)
- XAI reports + validation results
- Validated models
- Target infrastructure & system landscape
- SLA & monitoring requirements
- API interfaces + MLOps setup
Typische Szenarien
ML Deployment in Financial Services
Model moved from PoC to production API — with audit trail and monitoring.
Reproducible pipeline + audit documentation
Forecasting Models for Enterprise Decisions
Building a forecasting system with explainable recommendations.
XAI reports + live monitoring dashboard
Data Architecture for ML Readiness
Consolidation of fragmented data sources into an ML-ready infrastructure.
Integrated data pipeline + feature store