For a low code dashboard for 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). justineo/vue-echarts and c3js/c3 round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
We curate open-source GitHub repositories matching “low code machine learning dashboards”. Results are ranked by relevance to your query — pick filters below to narrow, or refine with AI.
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.
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.
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.
vue-echarts is a declarative charting wrapper and Vue.js component for the Apache ECharts library. It functions as a data visualization library that maps configurations and data updates to a rendering engine, enabling the embedding of interactive graphs and charts as reusable web components. The project provides a system for managing visual consistency through theme configuration and context-based injection. It allows for deep customization of the visualization interface, including the use of scoped slots to render custom HTML markup within tooltips and the construction of complex graphic ele
vue-echarts is a charting component for ECharts in Vue.js, excellent for embedding interactive visualizations but it is a data-viz building block, not a low-code ML dashboard builder — it offers no ML model integration, real-time monitoring, or no-code interface for building dashboards.
c3 is a charting library for creating reusable data visualizations and interactive charts based on the D3 JavaScript framework. It functions as a declarative visualization framework that generates complex charts through high-level configurations rather than manual SVG manipulation. The project provides a reusable chart component library and a tool for converting raw datasets into scalable vector graphics. These capabilities allow for the implementation of interactive data visualizations and web-based data reporting using standardized templates. The library supports the development of custom
c3 is a declarative charting library that helps create interactive visualizations, but it is a component for dashboards rather than a low-code ML dashboard builder — it lacks ML model integration, real-time monitoring, and the no-code interface this search requires.
Seaborn is a Python library designed for statistical data visualization. It functions as a high-level interface built on the Matplotlib ecosystem, providing specialized routines to explore and communicate complex patterns within datasets. The framework enables users to generate informative graphics through automated statistical aggregation, multi-plot faceting, and integrated regression modeling. The library distinguishes itself through a declarative approach to data mapping, which translates raw inputs into visual properties like color, size, and position. It includes a robust statistical tr
Seaborn is a Python static plotting library, not a low-code dashboard builder—you would need to write code to create visualizations with it, and it lacks real-time monitoring, model integration, and interactive dashboard features that this search targets.
Pygwalker is a library that transforms tabular data into interactive, drag-and-drop interfaces for exploratory analysis and visualization. It functions as a grammar-based framework that translates user interactions into declarative chart definitions, allowing for the creation of dynamic data exploration environments directly within notebooks or embedded web applications. The system distinguishes itself by offloading heavy analytical computations to backend kernels, which maintains responsiveness when visualizing large datasets. It supports the serialization of visual states into portable conf
Pygwalker lets you build interactive drag-and-drop data exploration dashboards with no code, but it focuses on general tabular data exploration rather than integrating ML models, real-time monitoring, or alerts, so it is more of a building block for ML dashboards than a full low-code ML dashboard builder.
This is a chart library for rendering interactive data visualizations within WeChat Mini Programs using the Apache ECharts engine. It provides a set of visual components that use 2D canvas interfaces to render line, bar, pie, and scatter plots. The library utilizes a 2D canvas interface to increase drawing speed and resolve visual layering issues. It implements an interactive charting component that supports touch-based data interaction, allowing users to see detailed contextual information through formatted tooltips when tapping or hovering over data points. The project covers mobile data v
This is a chart library for rendering data visualizations within WeChat Mini Programs, not a low-code ML dashboard builder — it lacks ML integration, real-time monitoring, and a builder interface, covering only one visualisation aspect of what you need.
nvd3 is a data visualization framework and reusable web graphing library. It provides a collection of interactive charting components built on top of the D3.js library to render complex datasets as graphics within a web browser. The library functions as a wrapper for D3.js, offering predefined chart types and modular templates. This implementation allows for the creation of custom data graphs and web dashboards without requiring the author to write low-level SVG code from scratch. The system utilizes SVG-based vector rendering and attribute-driven styling to generate visualizations. It incor
It is a charting library built on D3.js that provides interactive visualizations, which is a useful component for dashboards, but it lacks the low-code builder interface, ML model integration, and other features needed to assemble a full ML dashboard without coding.
Altair is a declarative data visualization library for Python that generates Vega-Lite specifications. It functions as a tool for mapping data to graphical marks using a high-level syntax, allowing users to describe the desired visual outcome instead of writing imperative drawing commands. The framework enables the creation of interactive charts and graphics, including linked views and filtered displays that respond to user input in real time. It supports the design of multi-view dashboards by combining visualizations into layered or faceted layouts. The library provides capabilities for sta
Altair is a declarative Python visualization library that can create interactive charts and dashboards, but it requires coding and lacks built-in ML model integration, real-time monitoring, and collaboration features, making it a component for data exploration rather than a complete low-code ML dashboard builder.
This project is a cross-platform mobile graphing library designed for rendering high-performance animated line charts and data visualizations. It functions as a canvas-based data visualization system and interactive charting component for mobile applications. The library focuses on animated data visualization, using interpolation to create smooth visual transitions between different data sets. It utilizes a GPU-accelerated graphics engine to maintain high frame rates and fluid transitions during data updates. The capability surface includes interaction systems for tracking pan gestures and d
This is a high-performance animated charting library for React Native, not a low-code ML dashboard builder — it provides visualization components but lacks the no-code interface, ML model integration, and full dashboard capabilities you need.
Lightdash is an open-source business intelligence platform that treats analytics logic as code. It centralizes metric and dimension definitions in a semantic layer, allowing data teams to define business metrics in YAML files version-controlled alongside data models. This approach ensures consistent, governed data access without requiring users to write SQL. Lightdash introduces CI/CD workflows for BI content, enabling teams to validate, test, and deploy analytics changes through automated pipelines and isolated preview environments. Its natural language query interface allows users to ask qu
Lightdash is a business intelligence dashboard builder with a low-code semantic layer and natural-language querying, but it is not designed for machine learning dashboards — it lacks ML model integration, real-time monitoring, and framework support.
| Repository | Stars | Language | License | Last push |
|---|---|---|---|---|
| streamlit/streamlit | 45K | Python | Apache-2.0 | |
| gradio-app/gradio | 42.9K | Python | Apache-2.0 | |
| paddlepaddle/visualdl | 4.9K | HTML | Apache-2.0 | |
| justineo/vue-echarts | 10.7K | TypeScript | MIT | |
| c3js/c3 | 9.3K | JavaScript | MIT | |
| mwaskom/seaborn | 13.7K | Python | bsd-3-clause | |
| kanaries/pygwalker | 15.6K | Python | apache-2.0 | |
| ecomfe/echarts-for-weixin | 7.5K | JavaScript | BSD-3-Clause | |
| novus/nvd3 | 7.2K | JavaScript | NOASSERTION | |
| vega/altair | 10.4K | Python | BSD-3-Clause |