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mlflow chart version 1.4.1 released

· 2 min read
Burak Ince
Burak Ince
Software Developer

We’re excited to announce the release of the mlflow Helm chart version 1.4.1, available as of August 6, 2025. This update comes with app version 3.2.0 and introduces enhancements, new features, and valuable contributions from our growing community to make deploying mlflow on Kubernetes even more seamless.

What’s New in mlflow 1.4.1​

This release includes important updates to both the Helm chart and the underlying mlflow application. Here’s a quick look at what’s new:

- 🔄 Updated: The container image burakince/mlflow has been upgraded to version 3.2.0
 → View Image on Docker Hub

Check out the full changelog and details in the official release notes.

How to Get Started with mlflow 1.4.1​

Getting up and running with mlflow 1.4.1 is easy. Our official Helm documentation offers a complete guide to help you deploy quickly and confidently.

Here’s what you’ll find:

  • Instructions for quick installation using default values
  • Customization options for various deployment needs
  • Tips and best practices for running mlflow in production

Explore the documentation and start your deployment today.

Why Use the mlflow Helm Chart?​

Supported and maintained by the GitHub Community Charts team, the mlflow Helm chart is purpose-built to streamline the deployment process on Kubernetes. Here’s why it stands out:

  • ✅ User-Friendly: Simplified setup and configuration
  • 🌐 Driven by Community: Actively updated with community contributions
  • ⚙️ Highly Configurable: Adaptable to a variety of use cases
  • 🛡️ Reliable: Tested thoroughly for Kubernetes environments

Whether you're deploying in dev or production, this chart provides the dependability you need.

Join the Community​

We’re always looking for contributors to help improve the mlflow Helm chart and other community-maintained charts.

Here’s how to get involved:

Thanks for being part of the open-source ecosystem. Together, we’re building better tools for deploying machine learning workflows on Kubernetes.