Building reliable machine learning pipelines with AWS Sagemaker and Comet
This tutorial is Part II of a series. See Part I here. Successfully executing machine learning at scale involves building reliable…
Codeless Deep Learning Pipelines with Ludwig and comet.ml
How to use Ludwig and comet.ml together to build powerful deep learning models right in your command line — using…
Building a DevOps Pipeline for Machine Learning and AI: Evaluating Sagemaker
The rush to build and deploy machine learning models has exposed cracks in traditional DevOps processes. Infrastructure built for…
Organizing Machine Learning Projects: Project Management Guidelines
Author: Jeremy Jordan Originally published at https://www.jeremyjordan.me on September 1, 2018, and was updated recently to reflect new resources. The goal…
Building a fully reproducible machine learning pipeline with comet.ml and Quilt
Classifying fruits using a Keras multi-class image classification model and Google Open Images Photo by Luke Michael on Unsplash This post was…
Approach pre-trained deep learning models with caution
Pre-trained models are easy to use, but are you glossing over details that could impact your model performance? How…
Seven Myths in Machine Learning Research
Photo by Samuel Zeller on Unsplash Note: this article originally appeared on Oscar Chang’s website at https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/ and was reposted here with his permission.…