Project-based data workflow
Organize work into projects and inspect image, timeseries, and tabular datasets in one place before building a model.
No Code AI is a no-code machine learning platform for building, evaluating, and deploying models from image, timeseries, and tabular data.
No Code AI is a no-code machine learning platform for building and deploying models without requiring coding or technical skills. The homepage positions it for domain experts, citizens, and scientists who want to work with AI through a visual interface rather than a traditional development stack.
The product organizes work into projects, where users can analyze image, timeseries, and tabular data, assemble preprocessing and ML blocks into pipelines, and run experiments to compare model versions. It also supports evaluation, model versioning, and deployment through REST API, automatic deployment, or downloadable code.
Organize work into projects and inspect image, timeseries, and tabular datasets in one place before building a model.
Assemble preprocessing steps, ML components, and custom blocks into drag-and-drop pipelines for experiments.
Create experiments to build, train, compare, and version multiple model variants within a project.
Evaluate models with built-in assessment workflows and add custom evaluation functions when needed.
Operationalize models by REST API, automatic deployment, or downloadable code wrapped in a ready-to-use interface.
Use step-by-step suggestions and AI recipes to guide pipeline creation and model selection.
Explore datasets visually, then organize them into a project before choosing the pipeline approach that fits the data.
Combine preprocessing steps, model components, and custom logic into a pipeline for a specific experiment.
Run multiple experiments, compare model versions, and choose the best-performing model for deployment.
Move a selected model into an application through REST API, automatic deployment, or exported code.
No Code AI is presented as a no-code platform for building, training, evaluating, and deploying machine learning models through projects and computational blocks.
The source highlights image, timeseries, and tabular data formats, with image support including .png, .jpg, and MRI images in NIFTI format.
The platform says models can be operationalized through a REST API, automatic deployment in your environment, or by downloading code wrapped in a ready-to-use interface.
The homepage shows a trial/signup flow, and the pricing page does not list public plan details in the captured text.
Traffic data is for reference only.
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