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<i>MLflow.js</i> is an open-source JavaScript library that helps developers track machine learning experiments and manage models with MLflow, providing functionalities for machine learning lifecycle in JavaScript/TypeScript environments.
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<br>
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## Features
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<i>MLflow.js</i> covers all REST API endpoints under MLflow's Tracking Server and Model Registry. Moreover, high-level abstractions have been developed to facilitate developers' common ML workflows. It provides some key advantages:
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<i>MLflow.js</i> covers all REST API endpoints under MLflow's Tracking Server and Model Registry. Moreover, high-level abstractions have been developed to facilitate developers' common ML workflows. It provides some key advantages:
| Stephany Ho |[](https://github.com/seneyu)|[](https://www.linkedin.com/in/stephanyho/)|
This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
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## Getting Started
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First, run the development server:
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bun dev
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```
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Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
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You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
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This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
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## Learn More
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To learn more about Next.js, take a look at the following resources:
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-[Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
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-[Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
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You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
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## Deploy on Vercel
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The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
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Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.
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Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
Copy file name to clipboardExpand all lines: mlflow-site/src/app/components/Demo.tsx
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];
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constdemoCardBlurbs=[
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'Create experiments with MLflow.js. Using built-in workflows, manage complex operations easily.',
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'Use MLflow.js to support a full ML project with TensorFlow.js. Log hyperparameters and key metrics during each training step. Evaluate model performance and register succesful models.',
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'Once the run completes, the MLflow UI provides powerful visualization tools to analyze experiments. Compare training and testing metrics across different runs to track performance patterns, or create custom charts that combine any logged hyperparameters and metrics to identify optimal model configurations'
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<>
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This example demonstrates how to use MLflow.js to support a full ML
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project with TensorFlow.js. It covers logging hyperparameters and metrics
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during training, evaluating model performance, registering high-performing
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models, and exploring results in the MLflow UI. Check out the full code
'Once the run completes, the MLflow UI provides powerful visualization tools to analyze experiments. Compare training and testing metrics across different runs to track performance patterns, or create custom charts that combine any logged hyperparameters and metrics to identify optimal model configurations',
Copy file name to clipboardExpand all lines: mlflow-site/src/app/components/Features.tsx
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constFeatures=()=>{
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constfeatureHeader='MLOps in Javascript, made simple.';
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constfeatureLongBlurb=`
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MLflow.js makes ML experimentation and model management seamless for JavaScript developers. Built with TypeScript, it provides intuitive access to MLflow\'s complete REST API while adding powerful abstractions for common ML workflows. Whether you\'re training models with TensorFlow.js, managing A/B tests, or monitoring production models, MLflow.js helps you track everything in one place.
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MLflow.js is an open source JavaScript client library, bringing MLflow's powerful capabilities to JavaScript and TypeScript environments. It makes machine learning experimentation and model management intuitive for JavaScript developers through a clean, Promise-based API. Built with TypeScript, it provides comprehensive access to MLflow's REST API while adding streamlined abstractions for common ML workflows. Whether you're tracking experiments with TensorFlow.js, automating retraining pipelines, or managing A/B tests, MLflow.js helps you organize and version everything in one place.
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