Cedric Renggli



Cedric Renggli

I am a Senior ML Researcher at Apple. My main research interest lies at the intersection of data management and AI/ML systems, with a particular focus on data-centric approaches to efficiently and reliably support the development, evaluation, and maintenance of AI/ML-based solutions.

Previously, I was a PostDoc in the Data Systems and Theory group lead by Dan Olteanu at UZH. I defended my thesis on Building Data-Centric Systems for Machine Learning Development and Operations at ETH Zurich's System Group under the supervision of Ce Zhang. I hold a bachelor's degree from the Bern University of Applied Sciences and received my MSc in Computer Science from ETH Zurich. My work on Efficient Sparse AllReduce For Scalable Machine Learning was awarded with the silver medal of ETH Zurich for outstanding master thesis. During my PhD, I have worked as a research intern and student research consultant at Google Brain (now Google DeepMind).

Selected publications


For a up-to-date list of all my publications check my profile on Google Scholar.

A Data Quality-Driven View of MLOps
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlaš, Wentao Wu and Ce Zhang
IEEE Data Engineering Bulletin March 2021
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
Cedric Renggli*, Luka Rimanic*, Luka Kolar*, Wentao Wu and Ce Zhang
IEEE International Conference on Data Engineering (ICDE) 2023
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu and Ce Zhang
Conference on Systems and Machine Learning (SysML) 2019
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
Cedric Renggli*, Luka Rimanic*, Nora Hollenstein and Ce Zhang
Neural Information Processing Systems (NeurIPS) 2021, Datasets and Benchmarks
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Cedric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic and Ce Zhang
International Conference on Very Large Data Bases (VLDB) 2023

Theses


PhD Thesis

Building Data-Centric Systems for Machine Learning Development and Operations
Cedric Renggli
 

MSc Thesis

Efficient Sparse AllReduce For Scalable Machine Learning
Cedric Renggli
Outstanding thesis award: Silver medal of ETH Zurich
 

Contact


 
Cedric Renggli
Apple Inc.