Serverless on Machine Learning: A Systematic Mapping Study
Bibliographic record
Abstract
Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry. It supports the development of machine learning (ML) pipelines typical in the phases of data collection, data pre-processing, building datasets, model training, hyper-parameters refinement, testing, and deployment to production. This complex pipeline workflow is a tedious process of iterative experimentation. Moreover, cloud computing services provide advanced features for managing ML stages and deploying them efficiently to production. Specifically, serverless computing has been applied in different stages of the machine learning pipeline. However, to the best of our knowledge, it is missing to know the serverless suitability and benefits it can provide to the ML pipeline. In this paper, we provide asystematic mapping studyof machine learning systems applied on serverless architecture that include 53 relevant studies. During this study, we focused on (1) exploring the evolution trend and the main venues; (2) determining the researchers’ focus and interest in using serverless on machine learning; (3) discussing solutions that serverless computing provides to machine learning. Our results show that serverless usage is growing, and several venues are interested in the topic. In addition, we found that the most widely used serverless provider is AWS Lambda, where the primary application was used in the deployment of the ML model. Additionally, several challenges were explored, such as reducing cost, resource scalability, and reducing latency. We moreover discuss the potential challenges of adopting ML on serverless, such as respecting service level agreement, the cold start problem, security, and privacy. Finally, our contribution provides foundations for future research and applications implying machine learning in serverless computing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".