This course is designed for practitioners in SMEs, startups, and applied research teams who need to turn data into actionable insights and repeatable, deployable ML workflows using high-performance computing (HPC) resources. Participants will learn practical patterns to build reproducible and scalable analytics and ML pipelines that remain reliable under real-world constraints, including messy data, growing volumes, performance bottlenecks, and execution across heterogeneous compute environments.
For teams preparing themselves to use HPC resources, the course provides a clear path from notebook prototypes to reproducible, job-based workflows. Participants will develop solutions in Jupyter Notebooks for rapid iteration and then operationalize them as script-based jobs suitable for production-style execution on high-performance systems. The hands-on curriculum covers scalable data processing with Apache Spark, performance-aware execution, and portable environments that help teams turn allocated compute time into measurable progress.
By the end of the course, participants will take back reusable assets they can apply immediately, including a project template (notebooks and job scripts), evaluation reports, scalable preprocessing pipelines, and runnable workflows that scale from laptop to HPC environments.
Fees
0 - 1690€