# mlflow **Repository Path**: wwj_2020/mlflow ## Basic Information - **Project Name**: mlflow - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-02 - **Last Updated**: 2025-09-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Open-Source Platform for Productionizing AI

MLflow is an open-source developer platform to build AI/LLM applications and models with confidence. Enhance your AI applications with end-to-end **experiment tracking**, **observability**, and **evaluations**, all in one integrated platform.

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## 🚀 Installation To install the MLflow Python package, run the following command: ``` pip install mlflow ``` ## 📦 Core Components MLflow is **the only platform that provides a unified solution for all your AI/ML needs**, including LLMs, Agents, Deep Learning, and traditional machine learning. ### 💡 For LLM / GenAI Developers

🔍 Tracing / Observability Trace the internal states of your LLM/agentic applications for debugging quality issues and monitoring performance with ease. Getting Started →	📊 LLM Evaluation A suite of automated model evaluation tools, seamlessly integrated with experiment tracking to compare across multiple versions. Getting Started →
🤖 Prompt Management Version, track, and reuse prompts across your organization, helping maintain consistency and improve collaboration in prompt development. Getting Started →	📦 App Version Tracking MLflow keeps track of many moving parts in your AI applications, such as models, prompts, tools, and code, with end-to-end lineage. Getting Started →

### 🎓 For Data Scientists

📝 Experiment Tracking Track your models, parameters, metrics, and evaluation results in ML experiments and compare them using an interactive UI. Getting Started →
💾 Model Registry A centralized model store designed to collaboratively manage the full lifecycle and deployment of machine learning models. Getting Started →	🚀 Deployment Tools for seamless model deployment to batch and real-time scoring on platforms like Docker, Kubernetes, Azure ML, and AWS SageMaker. Getting Started →

## 🌐 Hosting MLflow Anywhere

You can run MLflow in many different environments, including local machines, on-premise servers, and cloud infrastructure. Trusted by thousands of organizations, MLflow is now offered as a managed service by most major cloud providers: - [Amazon SageMaker](https://aws.amazon.com/sagemaker-ai/experiments/) - [Azure ML](https://learn.microsoft.com/en-us/azure/machine-learning/concept-mlflow?view=azureml-api-2) - [Databricks](https://www.databricks.com/product/managed-mlflow) - [Nebius](https://nebius.com/services/managed-mlflow) For hosting MLflow on your own infrastructure, please refer to [this guidance](https://mlflow.org/docs/latest/ml/tracking/#tracking-setup). ## 🗣️ Supported Programming Languages - [Python](https://pypi.org/project/mlflow/) - [TypeScript / JavaScript](https://www.npmjs.com/package/mlflow-tracing) - [Java](https://mvnrepository.com/artifact/org.mlflow/mlflow-client) - [R](https://cran.r-project.org/web/packages/mlflow/readme/README.html) ## 🔗 Integrations MLflow is natively integrated with many popular machine learning frameworks and GenAI libraries. ![Integrations](https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-integrations.png) ## Usage Examples ### Experiment Tracking ([Doc](https://mlflow.org/docs/latest/ml/tracking/)) The following examples trains a simple regression model with scikit-learn, while enabling MLflow's [autologging](https://mlflow.org/docs/latest/tracking/autolog.html) feature for experiment tracking. ```python import mlflow from sklearn.model_selection import train_test_split from sklearn.datasets import load_diabetes from sklearn.ensemble import RandomForestRegressor # Enable MLflow's automatic experiment tracking for scikit-learn mlflow.sklearn.autolog() # Load the training dataset db = load_diabetes() X_train, X_test, y_train, y_test = train_test_split(db.data, db.target) rf = RandomForestRegressor(n_estimators=100, max_depth=6, max_features=3) # MLflow triggers logging automatically upon model fitting rf.fit(X_train, y_train) ``` Once the above code finishes, run the following command in a separate terminal and access the MLflow UI via the printed URL. An MLflow **Run** should be automatically created, which tracks the training dataset, hyper parameters, performance metrics, the trained model, dependencies, and even more. ``` mlflow ui ``` ### Evaluating Models ([Doc](https://mlflow.org/docs/latest/model-evaluation/index.html)) The following example runs automatic evaluation for question-answering tasks with several built-in metrics. ```python import mlflow import pandas as pd # Evaluation set contains (1) input question (2) model outputs (3) ground truth df = pd.DataFrame( { "inputs": ["What is MLflow?", "What is Spark?"], "outputs": [ "MLflow is an innovative fully self-driving airship powered by AI.", "Sparks is an American pop and rock duo formed in Los Angeles.", ], "ground_truth": [ "MLflow is an open-source platform for productionizing AI.", "Apache Spark is an open-source, distributed computing system.", ], } ) eval_dataset = mlflow.data.from_pandas( df, predictions="outputs", targets="ground_truth" ) # Start an MLflow Run to record the evaluation results to with mlflow.start_run(run_name="evaluate_qa"): # Run automatic evaluation with a set of built-in metrics for question-answering models results = mlflow.evaluate( data=eval_dataset, model_type="question-answering", ) print(results.tables["eval_results_table"]) ``` ### Observability ([Doc](https://mlflow.org/docs/latest/llms/tracing/index.html)) MLflow Tracing provides LLM observability for various GenAI libraries such as OpenAI, LangChain, LlamaIndex, DSPy, AutoGen, and more. To enable auto-tracing, call `mlflow.xyz.autolog()` before running your models. Refer to the documentation for customization and manual instrumentation. ```python import mlflow from openai import OpenAI # Enable tracing for OpenAI mlflow.openai.autolog() # Query OpenAI LLM normally response = OpenAI().chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "Hi!"}], temperature=0.1, ) ``` Then navigate to the "Traces" tab in the MLflow UI to find the trace records OpenAI query. ## 💭 Support - For help or questions about MLflow usage (e.g. "how do I do X?") visit the [documentation](https://mlflow.org/docs/latest/index.html). - In the documentation, you can ask the question to our AI-powered chat bot. Click on the **"Ask AI"** button at the right bottom. - Join the [virtual events](https://lu.ma/mlflow?k=c) like office hours and meetups. - To report a bug, file a documentation issue, or submit a feature request, please [open a GitHub issue](https://github.com/mlflow/mlflow/issues/new/choose). - For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com) or join us on [Slack](https://mlflow.org/slack). ## 🤝 Contributing We happily welcome contributions to MLflow! - Submit [bug reports](https://github.com/mlflow/mlflow/issues/new?template=bug_report_template.yaml) and [feature requests](https://github.com/mlflow/mlflow/issues/new?template=feature_request_template.yaml) - Contribute for [good-first-issues](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) - Writing about MLflow and sharing your experience Please see our [contribution guide](CONTRIBUTING.md) to learn more about contributing to MLflow. ## ⭐️ Star History

## ✏️ Citation If you use MLflow in your research, please cite it using the "Cite this repository" button at the top of the [GitHub repository page](https://github.com/mlflow/mlflow), which will provide you with citation formats including APA and BibTeX. ## 👥 Core Members MLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members. - [Ben Wilson](https://github.com/BenWilson2) - [Corey Zumar](https://github.com/dbczumar) - [Daniel Lok](https://github.com/daniellok-db) - [Gabriel Fu](https://github.com/gabrielfu) - [Harutaka Kawamura](https://github.com/harupy) - [Serena Ruan](https://github.com/serena-ruan) - [Tomu Hirata](https://github.com/TomeHirata) - [Weichen Xu](https://github.com/WeichenXu123) - [Yuki Watanabe](https://github.com/B-Step62)