dstack.ai enables businesses to make quick data-driven decisions by making the work of data scientists easily accessible to the rest of the organization.
Reduces the time taken to collaborate between data scientists and other parts of the organization by allowing data scientists to instantaneously share, publish and get feedback on data reports such as dashboards and datasets with the rest of the organization.
Reduces the typical recurring cost associate with frontend and backend web and application development needed to otherwise publish data reports such as dashboard for rest of the organization
Allows non-technical audience in the organization to interact with data
1. Collaborate on results of an exploratory analysis such as data visualizations, dashboards, and datasets with team members or client:
Data scientists publish their results to the dstack.ai frontend using dstack libraries in their Python or R scripts
Published results can then be shared with team members or clients via URLs of the published results or simple email invitation.
In case of datasets, team members or clients can download the dataset either as CSV or as pandas dataframe.
Discuss on the published results using the comment section available for each published analysis
2. Track revisions of data analysis results:
Data scientists update the dataset and reload the dataset to dstack.ai frontend
Permitted users of dstack.ai frontend can view the new version along with older versions of the same dataset.
3. Build dashboard on top of exploratory visualizations:
Login to dstack.ai frontend
Build dashboard using the published data visualizations or tables
4. Automate publication of data reports [Experimental]
Data scientists define jobs on dstack.ai to be executed at regular schedules
Data reports such as datasets or visualizations are published at dstack.ai front end without manually running scripts.
Yes. The APIs offered by dstack.ai can be used from anywhere - Jupyter notebooks or RMarkdown.
Yes. The APIs offered by dstack.ai can be used from anywhere - Jupyter notebooks, RMarkdown, Python or R scripts, jobs or other applications - to publish or create data visualizations, datasets or dashboards.
dstack.ai is a complementary product to Jupyter notebooks. Once a data scientist has prototyped a “data science code”, dstack.ai allows for centralized management of data reports:
Publish reports to be used by a wider audience
Share prototypes also with non-technical audience in form of data reports such as data visualizations, dataset tables or dashboards
Keep track or get back to previous data reports
The in-cloud version of dstack.ai hosts your data securely at AWS.
The on-premise version of dstack.ai allows you to install the software locally, in your own servers or at a cloud service of your choice.
When you publish data, you can choose whether to make the data privately available to only those with whom you want to share the data.
dstack.ai uses API tokens to configure dstack profiles separately from your code. It allows you to make the code safe as it does not include plain secret tokens.
The free plan offers the support over a chat. The paid plans include the support over email.
Yes. Drop us an email at email@example.com and we will get right back to you.
Please look into our public roadmap to see if we have planned the feature. Please vote for the feature in case you want to expedite the development of the feature. If not, just drop us an email at firstname.lastname@example.org with the description and use case of the functionality and we will get back to you.
Yes. We offer an on-premises docker deployment which allows you to host dstack.ai either at your own premise or at a cloud service of your choice and control.