Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes
A data scientist builds a Streamlit or Dash prototype, the business wants to validate it, and the hard parts begin: getting access to live data, making the app available company-wide, and ensuring every user only sees what they are allowed to see. Following "best practices" turn a simple demo into weeks of platform work, leaving data scientists frustrated and blocking them from shipping apps to end users.
In this talk we will live-demo Merck's self-service app service we have developed and hardened over multiple years. It lets teams deploy Streamlit (and friends) in 3 minutes while meeting best practices like SSO, CI/CD, and governed data access control. The platform has become essential for Merck to ship data apps at scale: in 2025 it powered 750+ active apps reaching 8,000+ unique end users.
Under the hood, we show: how a use-case based access model enables scoped resource permissions so apps can safely access data on-behalf of the user. We also show starter templates that generate a deployable Git repo with example pages (e.g. Snowflake access or internal LLM chatbot). Finally, we cover the guardrails needed to operate this safely.
What you will learn: a cost-effective reference architecture based on AWS that you can adapt to your hyperscaler or platform, practical patterns for balancing the trade-off between central control and decentral freedom, and how templates and CI/CD help teams iterate quickly without compromising security or reliability.
This session is for anyone who has built a Streamlit (or Dash, R Shiny, FastAPI, React) prototype and then hit the wall when it needed to be shared with real users: access to live data, SSO, permissioning, deployment, and operational guardrails.
We will present the workflow and the architecture from both sides: as a data scientist shipping an app, and as a platform admin operating the service safely at scale.
What we will demo
We will demo the end-to-end workflow from zero to a running app using our internal app service. The platform includes a web console for self-service provisioning and configuration and the deployment runtime managing the state of the application.
- Using the web console to create and configure a new app from a framework template (Streamlit, Dash, R Shiny, FastAPI, React).
- How a Git repository is created and the first version is deployed behind the scenes, including a working starter app with example pages.
Key design decisions (the parts that are usually hard)
- Identity propagation: the app receives the signed-in user identity from SSO and uses it for downstream authorization.
- Authorization at the data layer: dataset permissions are scoped to use-case resource, making sure tokens can not be exploited.
- Safe multi-tenancy: per-app isolation plus resource limits to prevent noisy-neighbor problems.
- Repeatable delivery: templates plus CI/CD conventions so a new app starts from a working, deployable baseline.
- Day-2 operations: guardrails like quotas, rate limiting, and idle shutdown to keep the platform reliable and cheap.
Running at scale
- Production usage: 750+ active apps and 8k+ unique end users (2025).
- Infrastructure run rate under 10k USD per month (excluding engineering time).
Who should attend
- Data scientists and analysts who want to ship apps beyond a demo.
- Data platform and DevOps engineers building self-service tooling for governed environments.
- Teams standardizing how internal data & AI products are delivered to business users.
Takeaways
- For data scientists: what a good internal app hosting platform should provide, and which requirements you should ask your platform team for (governed on-behalf of data access, templates, CI/CD, guardrails).
- For platform teams: a blueprint you can adapt beyond AWS, including the architecture and tradeoffs necessary to operate fine-grained authorization and a multi-tenant runtime at scale.
If you do not have such an app platform in your company yet, use this talk as a checklist to start the conversation with your IT or platform teams. :-)
Bernhard is a Senior Data Scientist at Merck with a PhD in deep learning and over 7 years of experience in applying data science and data engineering within different industries. For more information you can connect with him on LinkedIn. 馃檪
As the Global Head of Platform Products Portfolio, Nicolas leads high performing teams that design, implement and maintain Merck's global data, analytics and AI ecosystem UPTIMIZE.