High-Performance Data Management Architectures for Scalable Machine Learning Pipelines in Cloud Ecosystems

Abstract

Modern machine learning (ML) at cloud scale demands data management architectures that can ingest diverse streams, deliver low-latency feature access, and support reproducible model training and deployment under stringent reliability and governance requirements. This paper proposes a reference architecture that unifies a lakehouse core with a real-time feature store, streaming ETL, and workload-aware storage tiers to balance throughput, latency, and cost. Batch and streaming data are consolidated via schema-evolving, ACID-compliant tables with time-travel for experiment repeatability, while changelog capture and event sourcing enable incremental model refresh and online learning. A control plane built on declarative orchestration coordinates data pipelines, validation, and lineage, and integrates MLOps primitives feature registries, model catalogs, and continuous training/validation backed by observability (data quality SLAs, drift, and freshness monitors). The design supports multi-region and multi-cloud deployments using portable formats and open table protocols, alongside vector indexes for retrieval-augmented generation and feature similarity search. We discuss placement strategies (serverless vs. provisioned), memory-optimized caches for online inference, and autoscaling policies that co-optimize performance and sustainability via workload shaping and tiered storage. A methodology is outlined for benchmarking end-to-end pipeline performance covering ingestion, transformation, feature serving, and model rollout together with governance and privacy controls (row/column-level security, PII tokenization, and federated patterns). The result is an actionable blueprint that enables teams to build resilient, auditable, and cost-efficient ML data planes capable of sustaining rapid iteration and production-grade reliability