peterschmidt

Great demo, congrats. I couldn’t find a link. Is one available? I’d like to try the plugin and learn more about it.

Hey, what you do is very cool! At dstack.ai, we are building an MLOps platform that in some way similar to your approach except we still love and leverage the concept of infrastructure as code! I’d love to chat to discuss the technology under the hood and other things if you are open. You can reach me at andrey at dstack.ai.

Ideally, experience of building or selling tools to developers. Understanding of MLOps scene would be a bonus. Desire to build great tools!

Im building dstack [1], a tool for continuous training ML models in the cloud. And I’m looking for a co-founder! Happy to chat if you are interested.

[1] https://dstack.ai

The one on the website if I recall correctly was mostly designed in house. Gonna double check that with the designer. The blog post image was taken from https://icons8.com/illustrations (free). There are actually a lot of free illustration libraries nowadays. One of my favorite is https://undraw.co/

One things is certainly that we would like our tool to be agnostic to data science tools and work with all of them. So you can use pretty much any visualization or ML library.

Another thing is that we’d like to eliminate the need to do any programming or HTML/CSS as much as possible.

Th jobs that are available as a part of the hosted solution is not yet part of the open-source library but this is certainly something for us to consider moving under open-source too.

We are currently at quite an early stage and a lot of work is still ahead. We’ll appreciate any feedback and suggestions on where to steer the roadmap.

Gonna work on preparing more use-case specific tutorials within coming weeks.

Yes, this Terms of Use covers only the use of dstack.ai (hosted version) and doesn’t cover the open-source tool.

The open-source tool is fully covered by Apache 2.0.

UPDATE: https://github.com/dstackai/dstack-react is outdated and was fully merged some time ago into the main repository https://github.com/dstackai/dstack. So we will delete https://github.com/dstackai/dstack-react within a week.

The open-source library is licenced under Apache 2.0 and has not any restrictions mentioned under the terms listed on dstack.ai. However, the terms on dstack.ai which you quote also sound odd to me. Truth to be told, the current terms were generated by one of the common templates provided for startups. After we put them than, we didn't have a chance to review again. Now that you brought it up, it's certainly time to revisit them. We certainly don't want to claim rights over any of user content. The exception is probably using the published content by the website itself to show it to the users according to the user's sharing settings. Gonna review the terms and come back with an update.

Sure,

1. Here's the most simple tutorial how to make an interactive dashboard and share it: https://docs.dstack.ai/tutorials/dashboards-tutorial It includes screenshots.

2. Here's another tutorial with more realistic data: Output: https://dstack.ai/gallery/d/b56128a3-522e-42d7-8662-9b1a768d... The code for it is available at https://github.com/dstackai/dstack-tutorials-py/blob/master/...

Actually we have very few examples. We gonna make more of them within this week.

Hi, thanks for the question. This feature is still in the design stage. The idea is pretty simple. Currently, you can push a pre-calculated visualization and associate it with particular user input. However, in many cases it's not possible to recalculate all possible combinations of user input in advance. That's why we'd like to let user push not a visualization but a function that produces a visualization. This function will be triggered when the user changes input. Such a function can do a visualization on the fly and if needed take the data from an external source.

I agree with your point. Reproducibility and versioning is an important yet ver challenging topic right now and not many seem to help with it. And it might be that the problem is not specifically about tools but rather the mindsets and workflows.

IMO dstack is a lot about process. Technologies can change. The process often stays. We’d like to find the best way to solve problems people face every day regardless a particular technology.

One more little thing which might be relevant is that dstack actually tracks revisions. What we haven't figured yet out is how to link the particular revision of the applications with the particular revision of the code / notebook.

Thank you very much. You're right, the README file needs improvements. We also don't have much tutorials yet that would show the tool from the practical point of view. Our short-term plans include: 1) Improving the documentation and writing more use-case specific tutorials; 2) Add more functionality for more interactive applications, including Machine Learning applications.

EDIT: Speaking of the react library, we've just finished a refactoring and plan to improve it too. Please don't hesitate to share your feedback, over email or via GitHub issues. And thank you!

We open-sourced it under Apache 2.0 which we find quite permissive and OS-community friendly. You're welcome to run a hosted version. We actually have it running on dstack.ai (it's free currently but we of course plan to have paid features if there is such a need).

Hi, I'm Peter, a part of the team. There is quite a few solutions already that try to make it easy to make data applications with Python or R. The most relevant solutions include Plotly Dash, Shiny, Voila, Streamlit. All of them are great projects even though all of them are very different. Our project is an attempt to explore this area and figure out what would be a way to build these applications without having a need to do programming, CSS, HTML, or deployment.

Basically, we want to make it possible to make data apps as simple as writing a few lines of code using only the libraries that data scientists already know - pandas, Matplotlib, scikit, Tensor, pytourch, etc. Ideally so you don't have to write your application code at all, and rather deploy your data science models and simply bind them with a simple UI logic. We believe the need to apply ML to enterprise use-cases will grow even more and tools like that will be very useful. Basically you'll be able to create an application that help your HR/Sales/Marketing/Product/<you name it> department to apply ML – in minutes, without the need to write this application, deploy or maintain.