ZenML

<!-- ZENML_GITBOOK_RELEASE_NOTES_START tag=0.96.2 -->

Known issues

Artifact version deletion via the API is broken in this release and will be fixed in 0.96.3. Artifact versions can still be deleted using the python SDK:

from zenml.client import Client

Client().delete_artifact_version(..., server_side=False)

Dynamic pipelines

• Explicit start ordering for dynamic steps: Dynamic pipelines now support start_after=... when calling steps, letting you control which concurrently launched steps should wait for others before starting. This makes it easier to model ordering constraints without turning concurrent parts of a dynamic pipeline into fully synchronous execution. Note that start_after is now a reserved step keyword, so steps that previously used a parameter with this name will need to be updated. PR #4995
• More flexible dynamic step inputs: You can now configure whether JSON-serializable raw values passed to steps in dynamic pipelines should be treated as parameters instead of artifacts. The new environment-variable threshold defaults to 0 to preserve existing behavior, while explicit APIs such as with_options(parameters=...) and ExternalArtifact(...) remain available when you want to force either behavior. PR #5079
• Improved dynamic execution semantics: Dynamic pipelines now support CONTINUE_ON_FAILURE execution mode, allowing already queued or asynchronous work to continue when an async step fails. ZenML also models implicit dependencies from newly launched steps to the last completed sync step, making mixed sync/async dynamic pipelines execute in a more predictable order. PR #5052

Integrations and deployment

• DigitalOcean integration: ZenML now includes a first-class digitalocean integration with support for DigitalOcean Spaces artifact stores and DigitalOcean Container Registry stack components. Spaces support builds on the existing S3-compatible implementation while handling DigitalOcean regions and endpoint generation for you. PR #5054
• Helm chart logging and OpenTelemetry configuration: The ZenML Helm chart now exposes server logging options and OpenTelemetry settings directly in values. You can configure console or JSON logging, service names, OTEL endpoints, and enable or disable traces, metrics, and logs without custom chart modifications. PR #5048

Data and metadata management

• Server-side artifact data deletion: ZenML can now delete artifact version data through the server API, not only from a full client with direct stack access. This enables artifact metadata and backing data to be deleted from the UI or from thin clients that do not have the artifact store stack component locally available. PR #5034
• Project metadata: Projects now support arbitrary project_metadata on create, update, and hydrated response models. Metadata is stored as portable JSON, preserved when omitted, replaced when explicitly supplied, and can be cleared by sending an empty object. PR #5086

Performance and scalability

• Lower-memory cross-filesystem copies: fileio.copy() now streams cross-filesystem copies in bounded chunks instead of reading the entire file into memory. This significantly reduces peak memory usage for local-to-remote and remote-to-local artifact operations, including PathMaterializer, directory copies, integration materializers, and code archive upload/download flows. PR #5031
• Fewer server requests during runs: ZenML now caches commonly reused project, store, stack, pipeline run, and completed step run responses

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<h3 align="center">One AI Platform From Pipelines to Agents </h3>

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<a href="https://zenml.io/projects">Projects</a> •

<a href="https://zenml.io/roadmap">Roadmap</a> •

<a href="https://docs.zenml.io/changelog">Changelog</a> •

<a href="https://github.com/zenml-io/zenml/issues">Report Bug</a> •

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🎉 For the latest release, see the <a href="https://docs.zenml.io/changelog">changelog</a>.

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---

ZenML is built for ML or AI Engineers working on traditional ML use-cases, LLM workflows, or agents, in a company setting.

At it's core, ZenML allows you to write workflows (pipelines) that run on any infrastructure backend (stacks). You can embed any Pythonic logic within these pipelines, like training a model, or running an agentic loop. ZenML then operationalizes your application by:

1. Automatically containerizing and tracking your code.
2. Tracking individual runs with metrics, logs, and metadata.
3. Abstracting away infrastructure complexity.
4. Integrating your existing tools and infrastructure e.g. MLflow, Langgraph, Langfuse, Sagemaker, GCP Vertex, etc.
5. Allowing you to quickly iterate on experiments with an observable layer, in development and in production.

...amongst many other features.

ZenML is used by thousands of companies to run their AI workflows. Here are some featured ones:

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<a href="https://www.airbus.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/66e826c67966c0e639be6591_airbus.svg" alt="Airbus" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.axa.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/66c84308916684f0d07b57ff_axa-min.svg" alt="AXA" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.jetbrains.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/682337dd23ca98ec293c2dc6_jetbrains-min.svg" alt="JetBrains" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://rivian.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/66e9897d1b1dc28e560c0c07_rivian-min.svg" alt="Rivian" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.wisetechglobal.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/65ddeac90f19eb6c4cd715f9_wisetech_logo-min.svg" alt="WiseTech Global" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.brevo.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/652d3e5d29d36f927c2bb623_brevo.webp" alt="Brevo" height="50"/></a>

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<a href="https://www.leroymerlin.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/65ddeac9b83eea2954b5a561_leroy_merlin_logo-min.svg" alt="Leroy Merlin" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.koble.ai/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/67e673e60161f59b5db6554c_koble.svg" alt="Koble" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.playtika.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/66e959c0c5f8422ecac8d81a_Playtika-min.svg" alt="Playtika" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://nielseniq.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/65ddeac959d7ca93745e8130_nielsen_iq_logo-min.svg" alt="NIQ" height="50"/></a>&nbsp;&nbsp;&nbsp;&nbsp;

<a href="https://www.enel.com/"><img src="https://cdn.prod.website-files.com/64a817a2e7e2208272d1ce30/66c84308b1e802ab9a246134_enel-min.svg" alt="Enel" height="50"/></a>

</div>

_{<i>(please email support@zenml.io if you want to be featured)</i>}

🚀 Get Started (5 minutes)

# Install ZenML with server capabilities
pip install "zenml[server]"  # pip install zenml will install a slimmer client

# Initialize your ZenML repository
zenml init

# Start local server or connect to a remote one
zenml login

You can then explore any of the examples in this repo. We recommend starting with the quickstart, which demonstrates core ZenML concepts: pipelines, steps, artifacts, snapshots, and deployments.

🏗️ Architecture Overview

ZenML uses a client-server architecture with an integrated web dashboard (zenml-io/zenml-dashboard):

• Local Development: pip install "zenml[local]" - runs both client and server locally
• Production: Deploy server separately, connect with pip install zenml + zenml login <server-url>

🎮 Demo

Here is a short demo:

Watch the video

🖼️ Resources

The best way to learn about ZenML is through our comprehensive documentation and tutorials:

• Documentation - Complete product documentation
• Your First AI Pipeline - Build and evaluate an AI service in minutes
• Starter Guide - From zero to production in 30 minutes
• LLMOps Guide - Specific patterns for LLM applications
• SDK Reference - Complete SDK reference

## 📚 More examples

1. Agent Architecture Comparison - Compare AI agents with LangGraph workflows, LiteLLM integration, and automatic visualizations via custom materializers
2. Deploying ML Models - Deploy classical ML models as production endpoints with monitoring and versioning
3. Deploying Agents - Document analysis service with pipelines, evaluation, and embedded web UI
4. E2E Batch Inference - Complete MLOps pipeline with feature engineering
5. LLM RAG Pipeline - Production RAG with evaluation loops
6. Agentic Workflow (Deep Research) - Orchestrate your agents with ZenML
7. Fine-tuning Pipeline - Fine-tune and deploy LLMs

🗣️ Chat With Your Pipelines: ZenML MCP Server

Stop clicking through dashboards to understand your ML workflows. The ZenML MCP Server lets you query your pipelines, analyze runs, and trigger deployments using natural language through Claude Desktop, Cursor, or any MCP-compatible client.

💬 "Which pipeline runs failed this week and why?"
📊 "Show me accuracy metrics for all my customer churn models"
🚀 "Trigger the latest fraud detection pipeline with production data"

Quick Setup:

1. Download the .dxt file from zenml-io/mcp-zenml
2. Drag it into Claude Desktop settings
3. Add your ZenML server URL and API key
4. Start chatting with your ML infrastructure

The MCP (Model Context Protocol) integration transforms your ZenML metadata into conversational insights, making pipeline debugging and analysis as easy as asking a question. Perfect for teams who want to democratize access to ML operations without requiring dashboard expertise.

🤖 Kitaru: Durable AI Agents

Building AI agents that need to survive crashes, pause for human approval, or run on cloud infrastructure? Kitaru is our open-source sister project for making Python agents durable.

• Crash recovery — checkpoint and replay from failure, not from scratch
• Human-in-the-loop — built-in approval gates and wait points
• Persistent memory — versioned, durable state across agent runs with full audit trail
• Framework agnostic — works with PydanticAI, CrewAI, or raw Python
• Runs anywhere — local, Kubernetes, Vertex AI, SageMaker, AzureML

Built on the same infrastructure that powers ZenML. Two decorators (@flow + @checkpoint) and you're done.

pip install kitaru

👉 kitaru.ai · GitHub · Docs

🎓 Books & Resources

<div align="center">

<a href="https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072">

<img src="docs/book/.gitbook/assets/llm_engineering_handbook_cover.jpg" alt="LLM Engineer's Handbook Cover" width="200"/>

</a>

<a href="https://www.amazon.com/-/en/Andrew-McMahon/dp/1837631964">

<img src="docs/book/.gitbook/assets/ml_engineering_with_python.jpg" alt="Machine Learning Engineering with Python Cover" width="200"/>

</a>

<a href="https://www.amazon.com/DeepSeek-Practice-fine-tuning-distillation-engineering/dp/1806020858">

<img src="docs/book/.gitbook/assets/deepseek-in-practice-thumb.jpg" alt="DeepSeek in Practice Cover" width="200"/>

</a>

</div>

ZenML is featured in these comprehensive guides to production AI systems.

🤝 Join ML Engineers Building the Future of AI

Contribute:

• 🌟 Star us on GitHub - Help others discover ZenML
• 🤝 Contributing Guide - Start with good-first-issue
• 💻 Write Integrations - Add your favorite tools

Stay Updated:

• 🗺 Public Roadmap - See what's coming next
• 📰 Blog - Best practices and case studies
• 🎙 Slack - Talk with AI practitioners

❓ FAQs from ML Engineers Like You

Q: "Do I need to rewrite my agents or models to use ZenML?"

A: No. Wrap your existing code in a @step. Keep using scikit-learn, PyTorch, LangGraph, LlamaIndex, or raw API calls. ZenML orchestrates your tools, it doesn't replace them.

Q: "How is this different from LangSmith/Langfuse?"

A: They provide excellent observability for LLM applications. We orchestrate the full MLOps lifecycle for your entire AI stack. With ZenML, you manage both your classical ML models and your AI agents in one unified framework, from development and evaluation all the way to production deployment.

Q: "Can I use my existing MLflow/W&B setup?"

A: Yes! ZenML integrates with both MLflow and Weights & Biases. Your experiments, our pipelines.

Q: "Is this just MLflow with extra steps?"

A: No. MLflow tracks experiments. We orchestrate the entire development process – from training and evaluation to deployment and monitoring – for both models and agents.

Q: "How do I configure ZenML with Kubernetes?"

A: ZenML integrates with Kubernetes through the native Kubernetes orchestrator, Kubeflow, and other K8s-based orchestrators. See our Kubernetes orchestrator guide and Kubeflow guide, plus deployment documentation.

Q: "What about cost? I can't afford another platform."

A: ZenML's open-source version is free forever. You likely already have the required infrastructure (like a Kubernetes cluster and object storage). We just help you make better use of it for MLOps.

🛠 VS Code / Cursor Extension

Manage pipelines directly from your editor:

<details>

<summary>🖥️ VS Code Extension in Action!</summary>

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<img width="60%" src="docs/book/.gitbook/assets/zenml-extension-shortened.gif" alt="ZenML Extension">

</div>

</details>

Install from VS Code Marketplace.

📜 License

ZenML is distributed under the terms of the Apache License Version 2.0. See

LICENSE for details.

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