Building ML-Ready Data Platforms on Cloud: Turning Experiments into Systems

Machine learning models rarely fail in production because of flawed algorithms. They fail because the underlying data platform lacks enforceable guarantees around ingestion, historical correctness, transformation logic, and observability. As ML systems mature, reliability depends on reproducibility, bounded freshness, and cross-team alignment. Organizations that treat data platforms as production infrastructure—not analytics tooling—reduce operational risk and build AI systems that scale sustainably.