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low-code ML dashboard builder

Classement mis à jour le 30 juin 2026

For un dashboard low-code pour le machine learning, the strongest matches are streamlit/streamlit (Streamlit is a low-code Python framework for turning data), gradio-app/gradio (Gradio is a Python library that turns ML model) and paddlepaddle/visualdl (VisualDL is a web-based deep learning experiment tracking dashboard). Each is ranked by relevance to your query, popularity and recent activity.

Nous sélectionnons les dépôts GitHub open-source correspondant à « low code machine learning dashboards ». Les résultats sont classés par pertinence par rapport à votre recherche — utilisez les filtres ci-dessous pour affiner, ou utilisez l'IA.

Résultats pour « un dashboard low-code pour le machine learning »

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • streamlit/streamlitAvatar de streamlit

    streamlit/streamlit

    44,982Voir sur GitHub↗

    Streamlit is a Python framework designed to transform data scripts into interactive web applications. It utilizes a reactive execution engine that automatically reruns scripts from top to bottom whenever a user interaction triggers a state change, ensuring the interface remains synchronized with the underlying data. By providing a declarative interface, it allows developers to build functional applications without requiring extensive knowledge of frontend web technologies. The framework distinguishes itself through an identity-based widget reconciliation system that persists user input across

    Streamlit is a low-code Python framework for turning data scripts into interactive web apps, directly matching the need for building ML dashboards with minimal coding and strong visualization, model integration, and real-time interactivity.

    PythonData Application FrameworksInteractive WidgetsExecution Models
    Voir sur GitHub↗44,982
  • gradio-app/gradioAvatar de gradio-app

    gradio-app/gradio

    42,931Voir sur GitHub↗

    Gradio is a Python library that enables the creation of interactive web applications by converting functions into browser-based interfaces. It functions as a declarative framework where developers define input and output components to automatically generate web forms, visualizations, and data-driven dashboards. By abstracting away manual web markup, the library allows for the rapid construction of interfaces for machine learning models, research demonstrations, and analytical workflows within a single environment. The platform distinguishes itself by automatically exposing internal applicatio

    Gradio is a Python library that turns ML model functions into browser-based interfaces with minimal code, directly supporting data visualization, model integration, and sharing—exactly what you need for building ML dashboards without heavy coding.

    PythonDeclarative UI FrameworksInteractive Interface BuildersModel Prototyping Tools
    Voir sur GitHub↗42,931
  • paddlepaddle/visualdlAvatar de PaddlePaddle

    PaddlePaddle/VisualDL

    4,882Voir sur GitHub↗

    VisualDL is a deep learning visualization toolkit and experiment tracking dashboard. It provides a web-based interface for monitoring training metrics, analyzing high-dimensional data, and rendering model architectures through static and dynamic graphs. The toolkit serves as a performance profiler to identify execution bottlenecks and optimize resource usage. It also functions as a data analyzer that uses projection algorithms to identify relationships between points in complex datasets. Capabilities include tracking training metrics via scalars and histograms, comparing multiple experiments

    VisualDL is a web-based deep learning experiment tracking dashboard that lets you monitor training metrics, compare runs, and explore high-dimensional data with minimal coding — it fits the low-code ML dashboard builder category, though its primary focus is deep learning rather than general ML.

    HTMLTraining Metric MonitorsVisualization ToolkitsDirected Graph Visualization
    Voir sur GitHub↗4,882

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