AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work shows how machine learning and GenAI can be used to assist with two specific tasks: First, when reading CSV files, it needs to be decided whether the first row is a header or not, and how machine learning and GenAI can be used to assist with two specific tasks.
This study discloses the model efficiency for different tasks, causes of failed fs tasks, and techniques for mitigating LLM failures, and will open source \phi-Bench to facilitate public research on using LLMs for fs development.
It is demonstrated how data scientists can leverage mlwhatif for a variety of pipelines and three different what-if analyses focusing on the robustness of a pipeline against data errors, the impact of data cleaning operations, and the impact of data preprocessing operations on fairness.
A fresh perspective is presented that turns parser errors -- traditionally seen as roadblocks -- into opportunities for generating valid, context-aware autocomplete suggestions, enabling domain-specific languages to provide basic development assistance with minimal overhead.
Synthetic data is extremely important in areas such as data quality, data cleaning, and machine learning. It enables the analysis of use cases in which real data is insufficient, unavailable, or distorted. However, generating synthetic data also presents challenges: The data must be as realistic as possible, but at the...
DataOrchestra, a framework that unifies different processing operations and orchestrates an example-specific pipeline for each example, is proposed and effective for math continued pretraining and outperforms stronger processing baselines, while reducing processing compute by skipping unnecessary downstream operations.
Extensive experiments show that DataMaster outperforms static baselines in most settings and surpasses full-pool training in a substantial number of cases, and simplifies data curation and removes the burden of manual strategy design.
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