A critical security vulnerability has been identified in ZenML versions prior to 0.46.7. This vulnerability potentially allows unauthorized users to take ownership of ZenML accounts through the user activation feature.
ZenML secures an additional $3.7M in funding led by Point Nine, bringing its total Seed Round to $6.4M, to further its mission of simplifying MLOps. The startup is set to launch ZenML Cloud, a managed service with advanced features, while continuing to expand its open-source framework.
We decided to explore how the emerging technologies around Large Language Models (LLMs) could seamlessly fit into ZenML's MLOps workflows and standards. We created and deployed a Slack bot to provide community support.
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.
Learn how to use ZenML pipelines and BentoML to easily deploy machine learning models, be it on local or cloud environments. We will show you how to train a model using ZenML, package it with BentoML, and deploy it to a local machine or cloud provider. By the end of this post, you will have a better understanding of how to streamline the deployment of your machine learning models using ZenML and BentoML.
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