Unsolved Problems in MLOps: Identifying Key Research Gaps

The paper surveys the current landscape of MLOps practices. It identifies several persistent challenges that lack robust solutions. Issues such as model reproducibility, data versioning, and monitoring are highlighted. The authors discuss the difficulty of scaling MLOps pipelines in production. They point out gaps in tooling for automated testing of machine‑learning models. The study calls for standardized benchmarks to evaluate MLOps methods. Recommendations include fostering interdisciplinary collaboration between engineers and researchers. The authors conclude that addressing these problems is critical for reliable AI deployment.