gradio VS re_data

There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.

It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.

What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?

Repo : https://github.com/gradio-app/gradio

There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.

I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )

I disagree hard with this – for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.

Gradio for a ui that your commanderGPT can visit and use

gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)

Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.

My script wouldn't work on the langchain because it was literally made within the gradio "docs builder" script that afaik was made specifically for their website (repo)

There’s also this open source tool I think is doing kind of what the OP is looking for, re_data. The source code lives here: https://github.com/re-data/re-data

What are the 5 hottest dbt Repositories one should star on Github 2022?

dbt is a software framework that sits in the middle of the ELT process. It represents the transformative layer after loading data from an original source. Dbt combines SQL with software engineering principles.

Here are my top5!

- Lightdash (https://github.com/lightdash/lightdash): Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

- ⏎ re_data (https://github.com/re-data/re-data): Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.

- evidence (https://github.com/evidence-dev/evidence): Evidence is another tool for lightweight BI reporting. With Evidence, you can build simple reports in "medium style" using SQL queries and Markdown.

- Kuwala (https://github.com/kuwala-io/kuwala): With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.

- fal ai (https://github.com/fal-ai/fal): Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models.

re_data ( https://github.com/re-data/re-data ) Re_data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project and which underlying metric is affected. In addition, the lineage graph is also intuitively displayed. Re-data is one of two others frameworks focusing on the observability aspect of lengthy pipelines in dbt (check also out: open-metadata and Elementary).

- ⏎ re_data: Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.

Some things you might be interested in are re_data and Elementary Data.

Around a year ago I launched re_data (an open-source data reliability tool) here. After some pivots, we seem to be getting traction and this is how it looks now: https://www.getre.io/. Super interested in getting your feedback and suggestions on the direction :)

Nice project, at re_data we just got over a lot of your new updates and it seems a quite large part of your project is "inspired" by code from our library https://github.com/re-data/re-data. Even with parts, we are not especially proud of ;)

If you decide to copy not only ideas but a big part of internal implementation, I think you should include that information in your LICENSE.

Cheers

It's more convenient when you are already using dbt and don't want to set up a separate workflow for testing data when it can be done with dbt inside the data warehouse. Also the thing re_data does well is letting you create time-based metrics about your data quality instead of just tests (a lot of the tests can be rewritten to that) That allows you to do a couple of things more than GE, you can for example easily visualize or look for anomalies in those. You can also compute tests much more efficiently. Research about computing metrics as a good way of doing data quality was actually done by the team behind deequ: http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf I'm the author, so obviously I'm a bit biased :)