ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. It has a simple, flexible syntax, is cloud and tooling agnostic, and has interfaces/abstractions that are catered towards ML workflows.
At its core, ZenML pipelines execute ML-specific workflows from sourcing data to splitting, preprocessing, training, all the way to the evaluation of results and even serving. There are many built-in batteries as things progress in ML development. ZenML is not here to replace the great tools that solve the individual problems. Rather, it integrates natively with many popular ML tooling, and gives standard abstraction to write your workflows.


Reproducibility of training and inference workflows. Managing ML metadata, including versioning data, code, and models. Getting an overview of your ML development, with a reliable link between training and deployment. Maintaining comparability between ML models. Scaling ML training/inference to large datasets. Retaining code quality alongside development velocity. Reusing code/data and reducing waste. Keeping up with the ML tooling landscape with standard abstractions and interfaces

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