Pipelines & Deployment

Step 8 – Pipelines & Deployment: making your workflow reusable

If a project is useful, you will want to run it again – or share it with colleagues. This step is about turning a one-off notebook into a repeatable pipeline or simple app. You organize your code, configuration, and models so that the same steps can be run reliably on new data, whether on your own workstation, a server, or eventually inside a hospital setting.

Technical name: Pipelines & Deployment

What this is

Move from one‑off experiments to something repeatable and usable by others:

• Standard steps from raw slide → prediction/report.
• A simple interface colleagues can try.
• Reproduce the same analysis months later.

Typical questions

• “If I give you a new batch, can we process automatically?”
• “Can we add a simple web front‑end?”
• “How do we make this reproducible next year?”

Common tasks

• Build pipelines: read → preprocess → tile → run model → aggregate.
• Package environments so dependencies don’t break.
• Add small apps/dashboards to interact with models.
• Schedule jobs/services for batch processing.

Core tools (examples)

• Snakemake / Nextflow — workflow rules like a lab SOP for data.
• Docker — package code + dependencies as a portable container.
• FastAPI / Flask — small web APIs for model access.
• Streamlit / Dash — simple web UIs.
• Git / GitHub — version control for code/configs and data recipes.

Clinician mental model

This is where a model stops being a one‑off and becomes a service or tool the department can actually use and trust.

Ready-to-use code

• PIPE-01: Streamlit + Docker starter — build a simple Streamlit UI, capture dependencies (requirements.txt), and package everything in a Docker image for portable, reproducible demos.