Challenges in Deploying Machine Learning: A Survey of Case Studies

Analysis

Why This Paper Matters

As machine learning moves from research to production, the gap between model development and reliable deployment has become a critical bottleneck. This survey systematically aggregates real-world deployment challenges reported across industries, offering a rare empirical grounding for the often anecdotal field of MLOps. By cataloging issues at each workflow stage, the paper gives practitioners a checklist of common pitfalls and researchers a clear agenda for targeted solutions.

The paper's value lies in its breadth: it covers use cases from healthcare to finance to autonomous systems, ensuring that identified challenges are not domain-specific but systemic. This cross-industry perspective is essential for building generalizable deployment frameworks.

Technical Contributions

• Comprehensive challenge taxonomy: The paper maps 28 distinct challenges to 8 deployment workflow stages (e.g., data management, model training, deployment, monitoring).
• Stage-by-stage analysis: For each stage, the authors summarize recurring issues such as data drift, reproducibility, latency constraints, and model decay.
• Research agenda: The paper proposes specific open problems, including automated monitoring, continuous integration for ML, and interpretability in production.
• No new metrics or experiments: The contribution is purely taxonomic and survey-based, not empirical.

Results

The survey does not present quantitative results or benchmarks. Its main finding is qualitative: every stage of the ML deployment workflow presents significant challenges, with no stage being trivial. The most frequently cited issues include data quality, model versioning, and monitoring for concept drift. The paper does not rank challenges by frequency or severity.

Significance

This survey has become a foundational reference for the MLOps community (638 citations). It legitimizes deployment challenges as a distinct research area and provides a common vocabulary for discussing them. By framing deployment as a multi-stage pipeline, it encourages holistic solutions rather than point fixes. The research agenda has influenced subsequent work on automated deployment pipelines, continuous training, and production monitoring systems.