Congratulations for your new laptop : ) What we mean by automatic model detection is that you don't have to add experiment tracking code to your notebook to "l

Congratulations for your new laptop : ) What we mean by automatic model detection is that you don't have to add experiment tracking code to your notebook to "log" most of the usual stuff such as model, parameters, metrics; we do that for you so you don't clutter your notebook. You can do it explicitly if you want to or if you're trying to log something in particular, but you mostly don't have to think about it, or generate experiment names, or worry about where you are logging your models. - You use a notebook to write code to train and evaluate your model - You schedule it (https://iko.ai/docs/resources/img/async.gif https://iko.ai/docs/resources/img/async.gif) - Your notebook is executed and the experiment is tracked (notebook, model, parameters, metrics). This way you can compare your runs. - You can then deploy your model to a "REST API" (https://iko.ai/docs/resources/img/deploy.gif https://iko.ai/docs/resources/img/deploy.gif), or build its Docker image and push it to use elsewhere (https://iko.ai/docs/resources/img/model_docker_image.gif https://iko.ai/docs/resources/img/model_docker_image.gif). Then you can monitor your deployed model's performance on a live dashboard. >So at some point I am not sure that Jupyter is going to be right. But I like it so maybe I can stretch out it's utility. Generally speaking, you use the notebook to train models and you use these models to return predictions. In the workflow I described above, the models produced are deployed and interacted with through requests.