arXiv (All)
open access
FlowMaker: From Tasks to Cost-Efficient Generative AI Workflow Deployments
Generative AI workflows execute user tasks by composing sequences of LLM calls, where each call performs a sub-task (e.g., summarization or translation), forming a workflow. Executing such workflows requires decisions over workflow structure, model assignments, and deployment configurations, which j
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Review of Generative AI and DevOps Pipelines: CI/CD, Agentic Automation, MLOps Integration, and LLMs#79821
- Signal from Structure: Exploiting Submodular Upper Bounds in Generative Flow Networks#1342401
- Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters#927797
- Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines#1105012
- MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering#1357999
Record · ID 1389807
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive