- 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.
Collaboration, Comet.ml, Data Operations, Data Workflow, DataOps, MLflow, MLOps, Pipeline, Reproducibility
- 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.
Agile, Andrew Ng, Comet.ml, Machine Learning, Software Engineering
- 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, Cross-validation, Machine Learning, Modeling, scikit-learn
- 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.
Comet.ml, Experimentation, Machine Learning
