Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.
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.
ZenNews is a tool powered by ZenML that can automate the summarization of news sources and save you time and effort while providing you with the information you need.
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.
By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
.gif)