18 citations · 18 across the 4 of their papers we have counts for
4 papers · 1 filter
ML Engineering Agents - A Data Management Perspective
Arnab Phani, Elias Strauss, Tim Schuster +1
Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language models for code generation and pred…
"Will This Data Break My Task?" - Interactive Synthesis of Task-Aware Data Unit Tests
Hao Chen, Arnab Phani, Sebastian Schelter
Data is a central resource for modern enterprises and institutions, and data errors propagating through data pipelines lead to serious impact in production. Therefore, data validat…
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads
Arnab Phani, Elias Strauss, Sebastian Schelter
Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated. LLMs enable a new type of workload, agentic pipeline sear…
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle
Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard +10
Machine learning (ML) applications become increasingly common in many domains. ML systems to execute these workloads include numerical computing frameworks and libraries, ML algori…