What is Liner?
Liner is a no-code machine learning tool that lets users train and export ML models using their own datasets. It allows people without coding or data science skills to create models for image, text, audio, or video classification, among other tasks. Users can import data, label it, and train models directly on their computer without needing a GPU. This no-code machine learning tool simplifies the training and deployment of ML models for beginners and non-technical users.
Features & Benefits
- No-code ML training – Train models using a simple interface without writing code or needing machine learning knowledge.
- Quick 3-step workflow – Import data, label samples, and train with one click for fast no-code machine learning setup.
- Multiple model types – Train models for image classification, object detection, audio classification, text classification, and more.
- Export-ready models – Export your trained models for use in apps, services, or edge devices with common deployment formats.
- Runs on CPU – Train ML models efficiently without needing GPU support or cloud compute resources.
- Edge device support – Deploy trained models to mobile and embedded devices for local inference.
- Ready-to-use datasets – Access a library of public datasets for quick training and experimentation.
- Optimized performance – Use fast and accurate training algorithms that complete model builds in minutes.
- Data privacy guaranteed – All data and training happen locally on your device to ensure user privacy.
- Template-based training – Start with predefined project templates for common ML tasks to simplify setup.
Liner Platforms
Windows, Mac
Liner Integrations
n/a
Real-world applications
A small business owner may use Liner to build a no-code machine learning model to detect products in shelf images. With the object detection feature, they can upload labeled images, train locally on their Mac or Windows machine, and then export the model for integration with a store-checking app.
A teacher creating a student project on AI might use Liner to train an audio classification model. Using animal sound datasets, they can help students understand how a no-code machine learning tool works without needing to learn Python or TensorFlow.
A mobile developer could use Liner to train a pose classification model to recognize yoga poses. They can download a labeled yoga dataset, train the model on their computer, and export it to a mobile-friendly format for app integration.
Content creators building video platforms may use Liner to classify uploaded video content by category. Using the video classification template, they can label clips by genre or activity, train a model with no coding, and use it to automate video sorting and tagging.
