Anil Inamdar

NetApp • 14K followers

Data Pipelines in ML Systems: Where Models Go to Survive Most ML projects don’t fail because the algorithm is wrong. They fail because the data is late. The schema changed. The feature drifted. Training and serving stopped matching. And the model kept predicting… quietly, confidently, incorrectly. That’s the real lesson of production ML: the model is the smallest part of the system. So what does production-grade ML actually look like? Data contracts come first Schema, semantics, freshness SLAs, lineage. Without contracts, systems don’t break loudly. They break silently. Validation is not a step, it’s a culture Ingestion. Features. Training inputs. Serving inputs. Predictions. If you validate only at the start, you are trusting the middle. And the middle is where problems hide. Feature stores reduce surprise Consistency between training and serving isn’t a nice-to-have. It’s survival. One definition of a feature, reused everywhere, prevents “it worked in training” disasters. Metadata is the connective tissue Which dataset? Which feature version? Which code commit? Which experiment? Without metadata you don’t have traceability. You have guesswork. Training isn’t production Training is an experiment. Shipping is an engineering workflow: gates, registries, staging, canaries, rollback. Batch and real-time must coexist The world is hybrid. Your architecture needs to be, too. Observability closes the loop Models decay. Data drifts. Behavior changes. Monitoring is the difference between value and silent damage. A simple antithesis to remember: Not model-first. System-first. What’s caused you the most pain in production: data quality, feature consistency, or deployment workflows? #MachineLearning #DataEngineering