Cecelia ShaoBuilding reliable machine learning pipelines with AWS Sagemaker and Comet.mlThis tutorial is Part II of a series. See Part I here.7 min read·Jul 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…5 min read·Jul 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.5 min read·Jul 17, 2019----
Cecelia ShaoICLR Reproducibility Interview #4: Aniket DidolkarReproducing h-detach: Modifying the LSTM Gradient Towards Better Optimization12 min read·Jul 2, 2019----
Cecelia ShaoinCometICLR Reproducibility Interview #3: Alfredo and RobertInterview by Cecelia Shao: Reproducing Variational Sparse Encoding11 min read·Jun 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 Images9 min read·May 13, 2019----
Cecelia ShaoinCometICLR Reproducibility Interview #1: Francesco, Samuel, EmiljanoReproducing the paper ‘Learning Neural PDE Solvers with Convergence Guarantees’ as part of the21 min read·Apr 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…3 min read·Apr 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…8 min read·Apr 24, 2019--1--1