DataOps Guides for ML and AI Pipelines

Version the dataset behind every evaluation

Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.

Prevent data leakage in ML and LLM evaluation datasets

Choose evaluation boundaries, keep related examples together, audit duplicate overlap, and retain reproducible split manifests for ML and LLM experiments.

Verified data pipelines for reliable AI agents

Design agent data paths that preserve source identity, versions, permissions, freshness, retrieval evidence, and verification results.

Build an LLM pipeline to classify ML failure reports

Use Polyaxon DAGs, tracked outputs, artifacts, and run comparison to develop a reviewable classifier for ML workload failure reports.

Evaluate with only the data available at decision time

Reconstruct evaluation features using event time and actual availability time, excluding late arrivals and later corrections while preserving the source versions in Polyaxon.

DataOps vs. MLOps: Differences and How They Work Together

DataOps delivers reliable data; MLOps delivers reliable ML systems. Compare their responsibilities, checks, and handoffs using a practical example.