DataNowKurs delivers concise, scenario-driven training that connects concepts to production realities. Each module is structured around a concrete case: from ingesting heterogenous datasets and building reproducible ETL pipelines, to versioning models and implementing CI/CD for ML. Participants work on guided labs that mirror typical Swiss business scenarios — for example, data consolidation for regional retail analytics, automated anomaly detection for utility meters, and model deployment for customer churn scoring. The emphasis is on replicable patterns, clear decision points and tools that integrate with common cloud and on-premise environments.
Rather than abstract theory, our curricula use step-by-step scenarios that show how workflows evolve as requirements change. Each lesson includes: a short case brief, data artifacts, an implementation sprint, and a follow-up evaluation focused on operational metrics such as pipeline latency, model refresh cadence, and observability coverage. Trainers illustrate activity-offs using real configuration files, pipeline DAGs and deployment manifests so learners leave with artifacts they can adapt to their own projects.
Our courses are tailored for data engineers, ML engineers, analytics leads and architects who need to move from prototypes to repeatable production workflows. Typical attendees come from SMEs and enterprise teams across management, retail and manufacturing in Switzerland. Through role-based labs and cross-functional scenarios, teams learn how to coordinate data contracts, testing strategies, feature stores and governance checkpoints while keeping delivery cycles short and traceable.
Start with a real case
Join a focused course at DataNowKurs that walks you through a complete workflow: data ingestion, transformation, model training, validation, deployment and monitoring. Each session centers on a practical case with artifacts you can reuse.
Structured learning by example
Case-driven lessons cover ingestion from APIs and batch sources, transformation with reproducible notebooks and DAG-based orchestration. Labs include schema evolution scenarios and troubleshooting common failure modes.
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Practical scenarios show how to version datasets and models, set up CI for training, and deploy models with monitoring. Exercises illustrate rollback, canary deployments and drift detection.
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We demonstrate governance through concrete scenarios: implementing access controls, data contracts between teams, lineage tracking and privacy-aware transformations for Swiss regulatory contexts.
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