When to run it
A model that works in a notebook and a business that wants it in a product, with no ML engineer free for the next quarter.
From notebookto monitored service.
Taking a notebook model to production
The playbook in one paragraph
A promising model lives in a data scientist’s notebook. Ana turns it into a reproducible pipeline, a tested serving endpoint and a monitored deployment - without rewriting the science.
Works with
- Jupyter or Databricks notebooks
- Git
- Training and serving infrastructure (SageMaker, Vertex AI, Kubernetes)
- CI/CD and monitoring
What Ana needs from you
- The notebook and data access
- Latency and throughput targets
- Where the model will be called from
How Ana runs it
Step by step.
5 steps from trigger to done - each one visible to your team as it happens.
- 01
Understand the notebook
Ana reads the notebook, separates data prep, feature engineering, training and evaluation, and confirms it can reproduce the reported results.
- 02
Make it a pipeline
Ana turns those cells into versioned, tested pipeline steps with pinned dependencies and tracked experiments.
- 03
Build the endpoint
Ana packages the model behind an API with input validation, batching where useful, and load tests against your latency target.
- 04
Deploy with monitoring
Ana ships it through CI/CD with drift, latency and error monitoring and a rollback path.
- 05
Hand over
Documentation, a model card and a runbook go to the owning team so the model is supportable from day one.
Who approves what
Approval gates
- Reproduced results
- Data scientist sign-off
- Production deploy
- Model owner
- Exposure to customers
- Product owner
What you get
Deliverables
- 01Reproducible training pipeline
- 02Tested, load-tested serving endpoint
- 03Monitoring and rollback
- 04Model card and runbook
What usually goes wrong
And how Ana handles it.
Risk
Results that do not reproduce
Ana confirms reproduction before productionizing and reports any gap with its cause.
Risk
Training–serving skew
Feature code is shared between pipeline and endpoint, and parity is tested.
FAQ
Common questions.
Not without asking. The goal is to productionize the data scientist’s model faithfully.
Keep going
Related playbooks.
AI / ML
Catching model drift and retraining safely
A production model quietly gets worse as the world changes. Ana detects the drift, diagnoses which features and slices moved, retrains on fresh data, and promotes the new model only if it beats the old one through shadow and canary.
Read the playbook →Software
Upgrading a framework across dozens of services
A major framework or runtime version is going end-of-life and it touches every service. Ana plans the migration, upgrades each service in its own branch, fixes what breaks, and hands your team small, tested pull requests instead of one terrifying diff.
Read the playbook →Software
Burning down a stale bug backlog
Every team has a backlog of real but low-priority bugs that never win a sprint. Ana triages them, reproduces the ones that are still real, fixes them with tests, and closes the rest with evidence.
Read the playbook →See it in your stack
Run this playbook on your systems.
Thirty minutes, your repositories and cloud, one real instance of this job.