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Liner Developer Platform
Pricing
- Model
- Free
Summary
Most ML tooling assumes you already know what a learning rate is — or at minimum that you have a GPU and an afternoon to configure a training environment. Liner exists for the gap before that: labeled data in hand, no engineering team, no cloud budget, no patience for Python.
Liner is a free desktop application for Windows and macOS that trains image, text, audio, video, and object detection models without writing code. You import labeled data, press train, and the tool selects an architecture and runs the job locally on your CPU — the vendor states training completes within minutes in most cases. Your data never leaves the machine, which matters for projects under privacy or compliance constraints. The export targets edge and mobile platforms, so the output is usable beyond the desktop. The ceiling arrives fast: there are no knobs to turn on architecture, no API to call from a pipeline, and no route to production at scale without rebuilding elsewhere.
Bottom line: Pick this to validate whether your labeled dataset can support a classifier before you spend engineering hours building a training pipeline — but plan to rebuild from scratch when your use case requires fine-grained model control, custom architectures, or serving predictions to more than one machine.
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Pros
Sign in to edit- Trains classifiers and object detectors on a CPU without a GPU, so teams without dedicated ML hardware are not blocked from running a first experiment.
- All training stays on the local machine and no data is sent to the cloud, which means projects under data residency or privacy constraints can use it without a legal review of third-party data processing.
- Automatic model selection removes the architecture decision entirely, so a domain expert with labeled data can reach a trained model without an ML engineer involved.
- Built-in dataset library gives a starting point when you do not yet have your own labeled data, cutting the time from install to first trained model.
- Edge-optimized model export means the output can run on mobile or embedded devices, so the prototype does not die at the desktop boundary.
Cons
Sign in to edit- Liner exposes no controls over model architecture, hyperparameters, or training configuration — when your dataset produces a weak model, there is nothing to adjust inside the tool, and the only path forward is moving to a framework like PyTorch or TensorFlow where you control the training loop.
- There is no API, no CLI, and no programmatic interface of any kind, so the tool cannot be embedded in a training pipeline, triggered by new data arrivals, or integrated into any automated workflow — teams that need reproducible, scheduled retraining abandon Liner entirely at that point.
- The vendor page does not specify which export formats or inference runtimes are supported, which means you cannot confirm deployment compatibility with your target environment until after you have trained the model — a costly discovery late in a prototype cycle.
- The tool is described as a beta download with community support as the only listed support channel, so production-blocking issues have no escalation path and no SLA — teams with deadline commitments tied to model delivery treat this as disqualifying.
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About
- Platforms
- Windows, macOS
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-25T13:23:22.674Z
Best For
Who it's for
- Users with no ML or coding background
- Rapid local model training on standard hardware
- Desktop-based ML prototyping and export
- Privacy-sensitive projects requiring local-only processing
What it does well
- Train image classifiers from labeled photos
- Build text classifiers for document or message categorization
- Create audio classifiers for sound event detection
- Develop object detectors for images
- Export models for mobile or edge device integration
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Frequently Asked Questions
- Is Liner Developer Platform free?
- Yes — Liner Developer Platform is fully free to use. There is no paid tier.
- Is Liner Developer Platform open source?
- Yes. Liner Developer Platform is open source.
- Can I self-host Liner Developer Platform?
- Yes. Liner Developer Platform supports self-hosting on your own infrastructure.
- What platforms does Liner Developer Platform support?
- Liner Developer Platform is available on: Windows, macOS.
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Curated lists that include this category
Liner is a free, locally-run desktop tool that takes labeled training data and produces an exported ML model without requiring any code or ML expertise. The workflow is three steps: import your data or pull from the built-in dataset library, press train and let Liner select and run an appropriate model automatically, then export the result to a platform your application can consume. Supported task types include image classification, text classification, audio classification, video classification, object detection, image segmentation, and pose classification — all selectable from project templates at the start of a session.
The defining characteristic of Liner is that training runs entirely on the local machine, including on standard CPUs without a dedicated GPU. The vendor states models are optimized for CPU training and that the data never leaves the computer. For teams handling sensitive documents, proprietary audio, or regulated imagery, this means the model development cycle stays fully air-gapped from any cloud service — a constraint that cloud-hosted AutoML tools cannot satisfy by design.
Liner fits a narrow but real use case: a domain expert who has labeled data and needs to test whether that data can support a classifier, without a machine learning engineer in the loop. It also fits privacy-sensitive prototyping where the cost of any cloud data transfer is unacceptable. It breaks down when a team needs to iterate on model architecture, tune hyperparameters, version experiments, integrate training into a CI/CD pipeline, or serve predictions to an external API — none of which the tool surfaces. Teams that outgrow the prototype stage will find no migration path inside Liner and will rebuild using a framework like PyTorch, TensorFlow, or a hosted AutoML service.
Exported models are described as edge-optimized and compatible with mobile and edge deployment targets, though the vendor page does not specify which export formats or runtimes are supported. The application is distributed as a native download for macOS (Intel), macOS (Apple Silicon), and Windows, and the vendor states it is free with no feature gating mentioned.