Continuous Machine Learning vs Neptune

Neptune vs Continuous Machine Learning: What are the differences?

Neptune: The most lightweight experiment tracking tool for machine learning. It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools; Continuous Machine Learning: CI/CD for Machine Learning Projects. Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Neptune and Continuous Machine Learning belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Neptune are:

• Experiment tracking
• Experiment versioning
• Experiment comparison

On the other hand, Continuous Machine Learning provides the following key features:

• GitFlow for data science
• Auto reports for ML experiments
• No additional services