Summary of Automating Code Adaptation For Mlops — a Benchmarking Study on Llms, by Harsh Patel et al.
Automating Code Adaptation for MLOps – A Benchmarking Study on LLMs
by Harsh Patel, Buvaneswari A. Ramanan, Manzoor A. Khan, Thomas Williams, Brian Friedman, Lawrence Drabeck
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the potential of Large Language Models (LLMs) in integrating Machine Learning Operations (MLOps) functionalities into machine learning training codebases. The study evaluates the performance of OpenAI’s gpt-3.5-turbo and WizardCoder models on various MLOps tasks, including adapting existing code samples with component-specific MLOps functionality, such as MLflow and Weights & Biases for experiment tracking, Optuna for hyperparameter optimization, and translating between different MLOps components. The results show that the gpt-3.5-turbo model outperforms WizardCoder in tasks like model optimization, experiment tracking, model registration, and hyperparameter optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can help with machine learning operations! This paper explores how these models can be used to simplify tasks like adapting code samples or translating between different MLOps components. The researchers tested two models, gpt-3.5-turbo and WizardCoder, on various MLOps tasks. They found that the gpt-3.5-turbo model is much better at these tasks than the WizardCoder model. |
Keywords
» Artificial intelligence » Gpt » Hyperparameter » Machine learning » Optimization » Tracking
