内容摘录
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<a href="https://mlflow.org/">
<img alt="MLflow logo" src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/logo.svg" width="200" />
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<h2 align="center" style="border-bottom: none">Open-Source Platform for Productionizing AI</h2>
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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Python SDK
PyPI Downloads
License
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<a href="https://mlflow.org/"><strong>Website</strong></a> ·
<a href="https://mlflow.org/docs/latest"><strong>Docs</strong></a> ·
<a href="https://github.com/mlflow/mlflow/issues/new/choose"><strong>Feature Request</strong></a> ·
<a href="https://mlflow.org/blog"><strong>News</strong></a> ·
<a href="https://www.youtube.com/@mlflowoss"><strong>YouTube</strong></a> ·
<a href="https://lu.ma/mlflow?k=c"><strong>Events</strong></a>
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🚀 Installation
To install the MLflow Python package, run the following command:
📦 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
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-tracing.png" alt="Tracing" width=100%>
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<a href="https://mlflow.org/docs/latest/llms/tracing/index.html"><strong>🔍 Tracing / Observability</strong></a>
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<div>Trace the internal states of your LLM/agentic applications for debugging quality issues and monitoring performance with ease.</div><br>
<a href="https://mlflow.org/docs/latest/genai/tracing/quickstart/">Getting Started →</a>
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-llm-eval.png" alt="LLM Evaluation" width=100%>
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<a href="https://mlflow.org/docs/latest/genai/eval-monitor/"><strong>📊 LLM Evaluation</strong></a>
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<div>A suite of automated model evaluation tools, seamlessly integrated with experiment tracking to compare across multiple versions.</div><br>
<a href="https://mlflow.org/docs/latest/genai/eval-monitor/">Getting Started →</a>
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-prompt.png" alt="Prompt Management">
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<a href="https://mlflow.org/docs/latest/genai/prompt-version-mgmt/prompt-registry/"><strong>🤖 Prompt Management</strong></a>
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<div>Version, track, and reuse prompts across your organization, helping maintain consistency and improve collaboration in prompt development.</div><br>
<a href="https://mlflow.org/docs/latest/genai/prompt-registry/create-and-edit-prompts/">Getting Started →</a>
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-logged-model.png" alt="MLflow Hero">
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<a href="https://mlflow.org/docs/latest/genai/prompt-version-mgmt/version-tracking/"><strong>📦 App Version Tracking</strong></a>
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<div>MLflow keeps track of many moving parts in your AI applications, such as models, prompts, tools, and code, with end-to-end lineage.</div><br>
<a href="https://mlflow.org/docs/latest/genai/version-tracking/quickstart/">Getting Started →</a>
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🎓 For Data Scientists
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-experiment.png" alt="Tracking" width=50%>
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<a href="https://mlflow.org/docs/latest/ml/tracking/"><strong>📝 Experiment Tracking</strong></a>
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<div>Track your models, parameters, metrics, and evaluation results in ML experiments and compare them using an interactive UI.</div><br>
<a href="https://mlflow.org/docs/latest/ml/tracking/quickstart/">Getting Started →</a>
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-model-registry.png" alt="Model Registry" width=100%>
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<a href="https://mlflow.org/docs/latest/ml/model-registry/"><strong>💾 Model Registry</strong></a>
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<div> A centralized model store designed to collaboratively manage the full lifecycle and deployment of machine learning models.</div><br>
<a href="https://mlflow.org/docs/latest/ml/model-registry/tutorial/">Getting Started →</a>
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-deployment.png" alt="Deployment" width=100%>
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<a href="https://mlflow.org/docs/latest/ml/deployment/"><strong>🚀 Deployment</strong></a>
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<div> Tools for seamless model deployment to batch and real-time scoring on platforms like Docker, Kubernetes, Azure ML, and AWS SageMaker.</div><br>
<a href="https://mlflow.org/docs/latest/ml/deployment/">Getting Started →</a>
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🌐 Hosting MLflow Anywhere
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<img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-providers.png" alt="Providers" width=100%>
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