Automated LLM Deployment and Evaluation: A Cloud-Native Approach Using LLM-as-a-Judge
Bibliographic record
Abstract
The rapid advancement of LLMs has led to widespread adoption across various domains, but it has also raised concerns about data security and privacy, particularly with publicly available and commercially operated platforms. Given their high computational demands, cloud environments are the obvious choice for deployment. As a result, organizations are increasingly deploying LLMs in confined cloud environments to protect sensitive data while leverazing scalable cloud resources. However, deploying LLMs in cloud environments remains a complex and time-consuming process that requires specialized skills and expertise in various areas, such as infrastructure management, resource allocation, and model setup. Testing and comparing LLMs to select the appropriate one is particularly challenging as different models are trained for different purposes, making the direct comparison nontrivial. Furthermore, differences in model architectures, training data, and fine-tuning strategies make objective evaluation difficult, limiting the effectiveness of traditional benchmarking approaches. To address these challenges, we present a cloud-native system that automates both the deployment and evaluation of LLMs. Our contributions are twofold: (i) we automate the provisioning and deployment of LLMs on various cloud platforms to stream-line infrastructure setup, and (ii) we develop a lightweight evaluation framework that leverages the LLM-as-a-Judge approach, where an independent LLM systematically assesses and compares different models based on predefined evaluation criteria. Our ongoing work aims to optimize LLM deployment by selecting cost-efficient cloud resources. We are also enhancing the evaluation framework with diverse prompts, broader metrics, and cross-model validation for fair, reproducible benchmarking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".