One Panel
OnePanel is a comprehensive cloud-based platform that simplifies machine learning workflows, catering primarily to computer vision tasks. By abstracting the complexities of infrastructure management,
What is One Panel?
OnePanel is a cloud-based platform designed to simplify machine learning workflows, particularly for computer vision tasks. It handles the infrastructure complexity so you can focus on building and training models rather than managing servers. The platform includes project and dataset management, Jupyter notebook workspaces, and containerised job execution. You can switch between CPU and GPU machines as needed, integrate with annotation tools like CVAT, and only pay for the compute time you actually use. It's built for teams and individuals working on image classification, object detection, video analysis, and similar computer vision projects.
Key Features
Project and dataset management
organise and track your ML projects with built-in dataset versioning
Jupyter notebook workspaces
interactive development environments for experimentation and prototyping
Containerised job execution
run training jobs in isolated containers without manual infrastructure setup
CPU and GPU switching
change compute resources based on task requirements without code changes
CVAT integration
work with annotation tools directly within the platform for labelled data workflows
Pay-per-second billing
charge only for active compute time rather than reserved capacity
Pros & Cons
Advantages
- Removes infrastructure management burden, letting you focus on model development
- Flexible compute options with straightforward switching between CPU and GPU
- Pay-per-second model means you don't overpay for idle resources
- Integrated annotation support speeds up the labelling workflow for computer vision tasks
Limitations
- Primarily focused on computer vision; less suitable if you work with other ML domains
- Cloud-only offering means all work depends on internet connectivity and vendor uptime
Use Cases
Training image classification models with datasets stored and managed in one place
Object detection projects that require switching between CPU prototyping and GPU training
Video analysis workflows combining annotation, processing, and model training
Collaborative computer vision work where team members need shared project access
Rapid experimentation with different model architectures in Jupyter notebooks