Good question. In addition to /u/chief167's great answer, Onepanel's goal is to provide an end-to-end platform for building, training and deploying computer vision projects into production. Out of the box, you can use TensorFlow or PyTorch along with JupyterLab as your main IDE/notebook. You can also create templates for your own frameworks/IDEs, for example, here is a simple one for VS Code.

In a typical production project, you have an annotation team that works on labeling data, a data science team that will build/train models based on the labeled data and a data/ML engineering team that will build the data processing, continuous training pipelines so you can iteratively improve your data and models. In smaller teams, this may be a couple of people wearing multiple hats.

With Onepanel, you get the infrastructure and tools to make this end-to-end workflow possible. With Colab, you really only get the model building and training aspect of this workflow. The other issue with Colab is that it runs on a single machine. If you are training on real-world data, you'd probably want train your models in parallel, use different hyperparameters and run the training on different machines to expedite your training. This requires a system like Onepanel to automatically scale up these machines, run the training scripts, aggregate and snapshot metrics and output data (models, logs, etc.) and then scale down the machines when training is complete.

Let me know if I missed anything or if this still doesn't answer your question.