Smoothly move through your data science steps, toggle between data products, incorporate new algorithms, broadcast your work in a consumable format, and share useful data artifacts (UDATs).
Focus on what you do best
Gain more time to focus on what you do best without the distractions of redundant work. Leverage the enterprise features of the platform to take care of basic, but critical functions.
Publish your code
Integrate your data science scripts (R, Python) into a data product to publish them as apps, making them runnable by domain experts in the analysis platform.
Dynamic analysis
Create guided, analysis apps that enable the domain experts to reparameterize on their own. This frees up your time to explore new analysis techniques.
Sharing
Get instant, detailed feedback from your domain experts on how your analysis apps are working.
History
Timestamp, rerun, and share your work from history whenever needed.
Versioning
Let Tag.bio automatically take care of all versions of data, scripts, and analysis apps.
Deployment
Rapidly deploy into your secure and compliant environment, in the cloud of your choice or on-premise.
Moving from a static to a dynamic workflow
Current, static workflow
Reparameterization requires the data scientist to rerun the whole process. Collaboration through file attachments is slow and stifles innovation.
New, dynamic workflow
The discovery process is accelerated when you give your users the ability to run guided analyses iteratively on their own and share their UDATs with their team in the same analysis platform.
Broadcast the results of your work to your audience
Allow all users to benefit from the work you’ve done in R, Python, and ML/AI by delivering your models in format usable and consumable by the domain experts.
Publishing an analysis app allows you to present the domain experts with a parameterizable data product. This reduces the number of requests that you get for parameter changes. You can review how the apps are experienced and iterate to come up with the best solution.
