- How to Make Remote Work Effective for Data Science Teams - Mar 23, 2020.
This post aims to highlight some work from home best practices, both general and data science-specific, in order to help data scientists and teams remain productive, connected and happy while working remotely.
- Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools - Jan 29, 2020.
Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.
- Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
- How to apply machine learning and deep learning methods to audio analysis - Nov 19, 2019.
Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.
- Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification - Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
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- Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.
- Comet.ml – Machine Learning Experiment Management - Apr 9, 2018.
This article presents comet.ml – a platform that allows tracking machine learning experiments with an emphasis on collaboration and knowledge sharing.