Summary of A Declarative System For Optimizing Ai Workloads, by Chunwei Liu et al.
A Declarative System for Optimizing AI Workloads
by Chunwei Liu, Matthew Russo, Michael Cafarella, Lei Cao, Peter Baille Chen, Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, Gerardo Vitagliano
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 A long-standing challenge in data management has been developing systems that can efficiently extract insights from large datasets without structured labels. Recent advancements have enabled high-accuracy fact extraction from various sources, including company documents, scientific papers, and image/video corpora. However, creating AI-powered queries still requires orchestrating multiple models, prompts, and operations, involving numerous decisions on model choice, inference methods, hardware, prompts, and more. This complexity changes as the query evolves and technical landscapes shift. In this paper, Palimpzest is introduced, a system that enables users to process analytical queries by defining them in a declarative language. The system optimizes query plans for runtime, cost, and output quality using its cost optimization framework. AI-powered analytics tasks are described, along with the optimization methods and prototype system. Evaluation on Legal Discovery, Real Estate Search, and Medical Schema Matching shows Palimpzest offers appealing plans, including one that is 3.3x faster and 2.9x cheaper than the baseline method while maintaining better data quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a superpower to quickly find answers from large collections of unstructured documents, images, or videos. Until recently, this was difficult and expensive. Now, AI models can do it with high accuracy. But, creating complex queries requires making many decisions about which model to use, what method to apply, and so on. In this paper, a new system called Palimpzest is introduced that makes it easy for anyone to create analytical queries by simply defining them in a clear language. The system optimizes the query plan to balance speed, cost, and quality. The authors tested Palimpzest on several tasks and showed that it can be faster and cheaper than existing methods while maintaining good results. |
Keywords
» Artificial intelligence » Inference » Optimization
