How I Rebuilt zenml.io in a Week with Claude Code
I rebuilt zenml.io — 2,224 pages, 20 CMS collections — from Webflow to Astro in a week using Claude Code and a multi-model AI workflow. Here's how.
Mar 3, 202616 mins
48 posts in this category
I rebuilt zenml.io — 2,224 pages, 20 CMS collections — from Webflow to Astro in a week using Claude Code and a multi-model AI workflow. Here's how.
ZenML's new Quick Wins skill for Claude Code automatically audits your ML pipelines and implements 15 best-practice improvements (from metadata logging to Model Control Plane setup) based on what's actually missing in your codebase.
ZenML's Pipeline Deployments transform pipelines into persistent HTTP services with warm state, instant rollbacks, and full observability—unifying real-time AI agents and classical ML models under one production-ready abstraction.
Learn how to build production-ready agentic AI workflows that combine powerful research capabilities with enterprise-grade observability, reproducibility, and cost control using ZenML's structured approach to controlled autonomy.
A technical deep dive into the performance optimizations that improved ZenML's throughput by 200x
Learn when to upgrade from open-source ZenML to Pro features with our subway-map guide to scaling ML operations for growing teams, from solo experiments to enterprise collaboration.
Discover the new ZenML MCP Server that brings conversational AI to ML pipelines. Learn how this implementation of the Model Context Protocol allows natural language interaction with your infrastructure, enabling query capabilities, pipeline analytics, and run management through simple conversation. Explore current features, engineering decisions, and future roadmap for this timely addition to the rapidly evolving MCP ecosystem.
Discover how ZenML implements the llms.txt standard to make ML documentation more accessible to both AI assistants and humans. Learn about our modular approach using specialized documentation files, practical integration with AI development tools, and how this structured format enhances the developer experience across different context window sizes.
ZenML 0.70.0 has launched with major improvements but requires careful handling during upgrade due to significant database schema changes. Key highlights include enhanced artifact versioning with batch processing capabilities, improved scalability through reduced server requests, unified metadata management via the new log_metadata method, and flexible filtering with the new oneof operator. The release also features expanded documentation covering finetuning and LLM/ML engineering resources. Due to the database changes, users must back up their data and test the upgrade in a non-production environment before deploying to production systems.
The ZenML team has addressed a security finding in ZenML Pro's role management system, reported by JFrog Security Research team. This update provides important information for users regarding role-based access controls and recommended actions
Playing around with some genAI services and tools to create a story and comic that showcases the journey of MLOps adoption for a small team.
Learn how to leverage caching, parameterization, and smart infrastructure switching to iterate faster on machine learning projects while maintaining reproducibility.
Shipping 🤗 datasets visualization embedded in the ZenML dashboard in a few hours
On the difficulties in precisely defining a machine learning pipeline, exploring how code changes, versioning, and naming conventions complicate the concept in MLOps frameworks like ZenML.
Exploring the evolution of MLOps practices in organizations, from manual processes to automated systems, covering aspects like data science workflows, experiment tracking, code management, and model monitoring.
How to use ZenML and dbt together, all powered by ZenML's built-in success hooks that run whenever your pipeline successfully completes.
We've open-sourced our new dashboard to unify the experience for OSS and cloud users, although some features are initially CLI-only. This launch enhances onboarding and simplifies maintenance. Cloud users will see no change, while OSS users can enjoy a new interface and DAG visualizer. We encourage community contributions to help us expand and refine this dashboard further, looking forward to integrating more features soon.
Community member Marwan Zaarab explains how and why he built a VS Code Extension for ZenML.
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.
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.
A winning entry - 3rd prize winner at Month of MLOps 2022 competition. Extraction of metadata from cheques using Transformers and ZenML.
A winning entry - 2nd prize winner at Month of MLOps 2022 competition.
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.
ZenML 0.23.0 comes with a brand-new experiment tracker flavor - Neptune.ai! We dive deeper in this blog post.
The ZenML MLOps Competition ran from October 10 to November 11, 2022, and was a wonderful expression of open-source MLOps problem-solving.
Transform quickstart PyTorch code as a ZenML pipeline and add experiment tracking and secrets manager component.
Test automation is tedious enough with traditional software engineering, but machine learning complexities can make it even less appealing. Using Deepchecks with ZenML pipelines can get you started as quickly as it takes you to read this article.
How to use ZenML and KServe to deploy serverless ML models in just a few steps.
ZenML combines forces with Great Expectations to add data validation to the list of continuous processes automated with MLOps. Discover why data validation is an important part of MLOps and try the new integration with a hands-on tutorial.
Getting started with distributed ML in the cloud: How to orchestrate ML workflows natively on Amazon Elastic Kubernetes Service (EKS).
How ZenML lets you have the best of both worlds, serverless managed infrastructure without the vendor lock in.
We built an end-to-end production-grade pipeline using ZenML for a customer churn model that can predict whether a customer will remain engaged with the company or not.
Connecting model training pipelines to deploying models in production is seen as a difficult milestone on the way to achieving MLOps maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker, Vertex AI, and AzureML! It’s normal to have certain steps that require specific infrastructure (e.g. a GPU-enabled environment) on which to run model training, and Step Operators give you the power to switch out infrastructure for individual steps to support this.
Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
Use MLflow Tracking to automatically ensure that you're capturing data, metadata and hyperparameters that contribute to how you are training your models. Use the UI interface to compare experiments, and let ZenML handle the boring setup details.
A dive into Python type hinting, how implementing them makes your codebase more robust, and some suggestions on how you might approach adding them into a large legacy codebase.
ZenML recently added an integration with Evidently, an open-source tool that allows you to monitor your data for drift (among other things). This post showcases the integration alongside some of the other parts of Evidently that we like.
All the advantages that ZenML will bring you if you choose to use it to productionize your model development workflows.
We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.
Why data scientists need to own their ML workflows in production.
An overview of some of the capabilities that ZenML will unlock for our users.
A set of guiding principles to help you better productionize your machine learning models.
Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.
