Cecelia ShaoBuilding reliable machine learning pipelines with AWS Sagemaker and Comet.mlThis tutorial is Part II of a series. See Part I here.Jul 31, 2019Jul 31, 2019
Cecelia ShaoCodeless Deep Learning Pipelines with Ludwig and Comet.mlHow to use Ludwig and Comet.ml together to build powerful deep learning models right in your command line — using an example text…Jul 24, 2019Jul 24, 2019
Cecelia ShaoinCometBuilding a DevOps Pipeline for Machine Learning and AI: Evaluating SagemakerThe rush to build and deploy machine learning models has exposed cracks in traditional DevOps processes.Jul 17, 2019Jul 17, 2019
Cecelia ShaoICLR Reproducibility Interview #4: Aniket DidolkarReproducing h-detach: Modifying the LSTM Gradient Towards Better OptimizationJul 2, 2019Jul 2, 2019
Cecelia ShaoinCometICLR Reproducibility Interview #3: Alfredo and RobertInterview by Cecelia Shao: Reproducing Variational Sparse EncodingJun 21, 2019Jun 21, 2019
Cecelia ShaoinCometBuilding a fully reproducible machine learning pipeline with Comet.ml and QuiltClassifying fruits using a Keras multi-class image classification model and Google Open ImagesMay 13, 2019May 13, 2019
Cecelia ShaoinCometICLR Reproducibility Interview #1: Francesco, Samuel, EmiljanoReproducing the paper ‘Learning Neural PDE Solvers with Convergence Guarantees’ as part of theApr 29, 2019Apr 29, 2019
Cecelia ShaoinCometIntroducing: ICLR Reproducibility Challenge Interview SeriesReproducibility is a powerful criteria for improving the quality of research. A result which is reproducible is more likely to be robust…Apr 24, 2019Apr 24, 2019
Cecelia ShaoinTowards Data ScienceProperly Setting the Random Seed in Machine Learning ExperimentsIn this post, we explore areas where randomness appears in machine learning and how to achieve reproducible, deterministic, and more…Apr 24, 20191Apr 24, 20191