ZenML is an extensible, open-source MLOps framework for using production-ready Machine Learning pipelines, in a simple way. At its core, ZenML will orchestrate your experiment pipelines from sourcing data to splitting, preprocessing, training, all the way to the evaluation of results and even serving.
While there are other pipelining solutions for Machine Learning experiments, ZenML is focused on the following:
ZenML solves the problem of getting Machine Learning in models to production. You should use ZenML if you struggle with:
Reproducing training results in production.
Managing ML metadata, including data, code, and model versioning.
Getting (and keeping) ML models in production.
Reusing code/data and reducing waste.
Maintaining comparability between ML models.
Scaling ML training/inference to large datasets.
Retaining code quality alongside development velocity.
Keeping up with the ML tooling landscape in a coherent manner.
A good place to go from this point is to:
Read more about core concepts to inform your decision about using ZenML
If you are familiar with the basics, jump right into the advanced guide section
If you're just not ready to use ZenML for whatever reason, but still would like to stay updated, then the best way is to star the GitHub repository! You can then keep up with the latest going-on's of ZenML, and it would help us tremendously to get more people using it.
Contributions are also welcome! Please read out contributing guide to get started.