Continuous Machine Learning vs baikal

baikal vs Continuous Machine Learning: What are the differences?

baikal: A graph-based functional API for building complex scikit-learn pipelines. It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines; 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.

baikal and Continuous Machine Learning can be primarily classified as "Machine Learning" tools.

Some of the features offered by baikal are:

• Build non-linear pipelines effortlessly
• Handle multiple inputs and outputs
• Add steps that operate on targets as part of the pipeline

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

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

baikal is an open source tool with 569 GitHub stars and 28 GitHub forks. Here's a link to baikal's open source repository on GitHub.