The new parliament is a radical departure in many ways. Its very hollywoodish if you happen to read the development.

Comments URL: https://news.ycombinator.com/item?id=41311717

Points: 31

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Comments URL: https://news.ycombinator.com/item?id=25007690

Points: 6

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I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai).

A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser.

Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot more open source data science frameworks than we integrated ourselves. For example, as a user, I want to push a matplotlib plot, a Tensorflow model, a plotly chart, a pandas dataframe, and I expect the presentation layer to fully-support it. Supporting all types of artifacts and providing all the tools to work with them solely seems to be a very challenging task. With this, we open-sourced the framework. Now you can build dstack locally, and run it on your servers, or in a cloud of your choice if that’s needed. More details on the project, how to use it, and the source code of the server can be found at the https://github.com/dstackai/dstack repo. The client packages for Python and R are available at the https://github.com/dstackai/dstack-py and https://github.com/dstackai/dstack-r correspondingly.

What’s next: User callbacks- so that application shows not just pre-calculated visualizations but also can fetch data from a store and process it in real-time. ML models- so that data scientists can publish a stack which binds together a pre-calculated ML model and user parameters Use cases- Support specific use cases that help data scientists to build data science models into data applications as fast as possible.

We would be happy to get your feedback on the open-source framework and also get your opinion on what kind of use cases can be built on top of the framework? Thank you.

Comments URL: https://news.ycombinator.com/item?id=24131723

Points: 134

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Comments URL: https://news.ycombinator.com/item?id=22899337

Points: 6

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Comments URL: https://news.ycombinator.com/item?id=22795071

Points: 8

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Comments URL: https://news.ycombinator.com/item?id=22772478

Points: 7

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Comments URL: https://news.ycombinator.com/item?id=22727268

Points: 13

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Comments URL: https://news.ycombinator.com/item?id=22638652

Points: 1

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I am Riwaj from dstack.ai. dstack.ai offers a convenient way to publish, share, and track data visualizations, analysis results, and data reports.

In our experience, as of now, many data analysts and data scientists work through the data in various tools such as excel, Jupyter notebook, etc. and then share the graphic with their peers, managers or clients using local network drives or emails in the form of PDFs or PowerPoints. There is also a growing number of users and teams who use BI Tools, Tableau, Superset or build custom applications to organize interactive dashboards and reports. When it comes to tracking the visualizations or analyzing results, some teams resort to work around methods such as printing the result in a document and then using version control tools over the document. In summary, there are a variety of tools available for exploring, tracking and sharing reports, but many of these allow to publish reports generated by their own tools thus allowing for very little flexibility for a team that works with multiple programming languages, tools, and plotting libraries.

We at dstack.ai have built a light-weight solution where you can publish the data visualizations via APIs from anywhere (a Jupyter notebook, a script or an application) and store them in our web application (which is also mobile friendly) for collaboration with teams and clients, i.e., you can share the published charts via URL and comment on each stored chart (think of Instagram). Our current version supports Python and R as programming languages, and Matplotlib, Ggplot2, Plotly and Bokeh as plotting libraries. In our first version, we only support static charts and basic interactive charts, i.e., if you stored charts with certain parameters, the users who browse through those charts can play with only those specific parameter values.

We think there is a lot more one can offer to improve the current process of a collaborative exploration of data visualization for teams. As basic as our current solution is, we are currently working on releasing new features such as - Organizing the charts into dashboards - User groups and permission management - Plugins for spreadsheets such as Google docs and MS Excel - Dynamic plots and dashboard that fetches data from a data source and can be scheduled to update regularly - A mobile application to access the visualizations, - On-prem options

We would love to hear about your challenges in the area of collaboration around data visualization, and receive any feedback on what we currently have. For your reference, here is the documentation for product usage. https://docs.dstack.ai

Comments URL: https://news.ycombinator.com/item?id=22607035

Points: 20

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Comments URL: https://news.ycombinator.com/item?id=21980254

Points: 1

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