Morris Stallmann

Düsseldorf, Germany & Maastricht, The Netherlands

Machine Learning & MLOps Engineer | Decision Systems Architect

Building robust and scalable machine learning and optimization systems that drive business impact.

About Me

I am a Machine Learning, Optimization, and MLOps engineer specializing in designing, deploying, and optimizing intelligent decision-making systems. With a passion for principled approaches to transforming data into decisions, I help organizations unlock value through automation, predictive analytics, optimization, and robust architectures.

Skills & Tech Stack

Machine Learning

Skills: Problem framing, experimental design, productionization, federated learning, supervised and unsupervised learning

Tech: Python, Flower, AutoML, scikit-learn, SparkML, Pytorch, XGBoost, LightGBM, SHAP, Optuna

MLOps

Skills: CI/CD/CT, IaC, drift detection and handling, code, data, and artifacts version control, automated testing, alerting, monitoring, experiment tracking, data governance

Tech: GitHub Actions, Terraform, Lakehouse Monitoring, Unity Catalog, MLflow, Weights & Biases, pytest, SonarQube

Data Engineering

Skills: ETL pipelines, job orchestration, graph processing, parallel processing

Tech: Data Factory, Databricks jobs, Python, Spark, Networkx

Optimization
Decision modeling, graph algorithms, linear programming, heuristics, Gurobi
Cloud & Platforms
Databricks, Azure
Architecture
End-to-end ML and optimization systems, batch and live model serving, secure integration with existing systems

Services

Selected Projects

Improving Customer Onboarding Experience in Electric Vehicles

Problem: Customers are unsatisfied with startup times of their infotainment systems.

Solution: Developed a predictive system identifying user routines to quickstart the infotainment system, integrated into enterprise architecture.

Outcome: Improved user experience for >>100k customers.

Tech Stack: Azure, Databricks, Data Factory, Azure API Management, Python, Spark, SQL, Terraform, MLflow, Optuna

View official VW communication

Automating Machine Learning Deployment, Testing, and Operations

Problem: Manual deployment, testing, and operations are time-consuming and error-prone.

Solution: Built a CI/CD/CT-enabled MLOps platform with automated testing, monitoring, and alerting.

Outcome: Reduced model update time from (sometimes) days to minutes; reduced time until issues are noticed.

Tech Stack: Azure, Databricks, Lakehouse Monitoring, MLflow, model serving endpoints, GitHub Actions, Terraform, SonarQube, Docker

End-to-End Unsupervised Machine Learning System Informing Product Strategy

Problem: Product usage data is underutilized in product strategy decisions.

Solution: Built and integrated an unsupervised machine learning system identifying distinct groups of product usage behavior.

Outcome: Increased data utilization with a positive impact on product decisions.

Tech Stack: Azure, Databricks, Python, Terraform, SQL, MLflow, Spark, SparkML, Optuna

Optimization Engine for Fiber Network Planning

Problem: Manual evaluation of fiber expansion areas can be time-consuming.

Solution: Designed and implemented a decision-support tool based on linear programming and heuristics.

Outcome: Reduced evaluation times from days to hours.

Tech Stack: GCP, Python, Gurobi, Docker, Docker Swarm

Building a Promotional Pricing Simulation Tool

Problem: Promotional pricing does not always yield the desired results.

Solution: Implemented a web-based pricing simulation tool based on explainable machine learning and optimization.

Outcome: Improved decision-making and increased profit margins.

Tech Stack: GCP, Python, Django, SQL, Docker

Contact & Links