zenml

Getting started with your ML project work is easier than ever with Project Templates, a new way to generate scaffolding and a skeleton project structure based on best practices.

Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.

A set of guiding principles to help you better productionize your machine learning models.

ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.

Explore how ZenML, an MLOps framework, can be used with large language models (LLMs) like GPT-4 to analyze and version data from databases like Supabase. In this case study, we examine the you-tldr.com website, showcasing ZenML pipelines asynchronously processing video data and generating summaries with GPT-4. Understand how to tackle large language model limitations by versioning data and comparing summaries to unlock your data's potential. Learn how this approach can be easily adapted to work with other databases and LLMs, providing flexibility and versatility for your specific needs.

The 0.40.0 release introduces a completely reworked interface for developing your ZenML steps and pipelines. It makes working with these components much more natural, intuitive, and enjoyable.

We released an updated way to deploy MLOps infrastructure, building on the success of the `mlops-stack` repo and its stack recipes. All the new goodies are available via the `mlstacks` Python package.