arXiv:2606.14361v1 Announce Type: new Abstract: Machine learning (ML) pipelines require extensive data preparation, feature engineering, and integration across heterogeneous sources, making them tedious and error-prone to develop. While large language models (LLMs) have recently shown promise for assisting programming tasks, chat-based interfaces provide limited control over pipeline behavior and often produce code that is difficult to optimize or integrate into production systems. We demonstrate SemPipes, a novel programming model that extends ML pipelines with declarative, LLM-powered semant
The increasing complexity of ML pipelines and the rapid advancement of LLMs are driving a need for more efficient and controllable AI-assisted programming tools.
Improving the development and integration of ML pipelines through LLM-powered interfaces can significantly accelerate AI adoption and reduce errors in critical applications.
The interaction model for building machine learning pipelines shifts from purely chat-based to more declarative and integrated LLM assistance, enhancing control and optimization.
- · AI developers
- · ML platform providers
- · Enterprises adopting ML
- · Data scientists
- · Manual pipeline developers
- · Companies with inefficient ML integration strategies
Increased efficiency in developing complex machine learning systems.
Faster deployment of AI solutions across various industries, leading to new AI-powered products and services.
Enhanced competition among AI tool providers, driving further innovation in developer-facing AI technologies.
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Read at arXiv cs.LG