XGBoost vs Continuous Machine Learning: What are the differences?

XGBoost: Scalable and Flexible Gradient Boosting. Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow; 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.

XGBoost can be classified as a tool in the "Python Build Tools" category, while Continuous Machine Learning is grouped under "Machine Learning Tools".

Some of the features offered by XGBoost are:

• Flexible
• Portable
• Multiple Languages

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

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

XGBoost is an open source tool with 19.4K GitHub stars and 7.61K GitHub forks. Here's a link to XGBoost's open source repository on GitHub.