FPLN for Data-Science Workloads

Documentation status: guide — see Maturity and evidence.

FPLN can participate in data-science execution when a workload combines functional transformation, logical constraints, parallel execution, or neural computation over shared conceptual context.

Use FPLN when the computation benefits from remaining inside the Runtime execution model. Use an external engine when specialist libraries or operational constraints make that a better boundary.

Do not choose an execution engine solely from language preference; choose according to capability, data movement, latency, governance, and traceability.