johannesbeil

Yes, plus they might just have some data that looks that it might somehow work and they just wanted to slap a patent on it in case it ever works.

Do you think a Github for science would help?

Congrats! Can you maybe say a bit about how you trained it?

Thanks! Feel free to get in touch, maybe we can exchange ideas.

Of course, thank you for asking. An ordinary notebook only lets you order notes linearly. As soon as you try out different paths and switch back and forth between them, for example, different models, you can lose the overview. With the graph, for any result, you can always go back an understand exactly where it came from. We make this extra easy by letting you plot the notes according to the parameters they are tagged with. You can try it in the live demo by clicking on the graph button on the top left and select "model_name" and "mae" to compare how the models performed. Besides this (compared to Evernote or a paper notebook), we also have a python client that lets you integrate documentation directly into taking data from equipment or data science.

Noted, thank you for your feedback!

No, if you have cookies disabled it can't log on to the live demo. As mentioned before, it's not a chatbot, it is just a way for you to get in touch with us right out of the app without opening an email client. If we are around we will also answer in the chat directly.

Yes, only Chrome and Safari are fully supported right now. It should tell you actually. Thanks for asking!

Thanks! It is, of course, new, but also less rough when you use the actual product and not the live demo.

Noted, thanks!

Agreed!

Thanks, yes, we have to fix that.

Thank you for your feedback! The main advantages of the graph are: 1. you can get an easier overview of complex processes and how they connect. 2. The tracked parameters are inherited. So if you are tracking parameters that don't change to the next iteration, you will still have them stored in the process. It also remembers how they connect, so if you change a name upstream it will cascade down. 3. You can plot the leaves according to their parameters. If you click on the plotting button in the top left and choose model_name vs. mae for example, you can compare how different models performed and still see how their iterations connect.

Thanks! Sorry to hear, but we need those tools. We are an EU company and thereby regulated by GDPR, so we are obligated to treat your data very responsibly. You can learn more about the product (without intercom and sentry) on our blog: https://medium.com/amie-ai/amie-use-case-data-driven-winemak... Feel free to get in touch!

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We're happy to release the beta of our app and python client today. Every day, valuable data and results get lost and forgotten. amie provides you with an easy way to structure and store your data and their relationships, to make sure your workflow is fully reproducible and make it easy for you to keep an overview. amie works with any data-driven task, from data-science to beer brewing.