CML (short for ‘Continuous Machine Learning’) is a new piece of software from the makers of DVC (Data Version Control) that makes it easier to use continuous integration approaches in machine learning projects. Continuous integration is an approach to building software where all members of a team integrate their work regularly. Integrations are verified by automated builds including tests so that errors get picked up quickly. This approach can make it much easier for teams to work together on larger projects.

For various reasons this has been hard to do with machine learning, CML should change this:

Use it to automate parts of your ML workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. Let’s bring the power of DevOps to ML or MLOps.

Data scientists are increasingly using tools and technique that have long been standard in software development. Mostly this is great news, although I do have some reservations about data science becoming a subfield of software development (software does eat everything eventually…) I will be trying this out and would love to hear from anybody else who does.

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