The document discusses the templates and deployment process used at the company for machine learning models. It describes two types of models that are built: online models that are served via API, and batch models that process data from a data lake. The company provides templates to help data scientists develop models according to guidelines and integrate them with services. It also describes Sheep, an in-house Python library that standardizes the deployment process to make it easier for data scientists by automating tasks like security, data gathering, and service management. The goal is to reduce the amount of code data scientists need to write to deploy a model.

1. Sheep - Model
Deployment
Toolkit
12/18/2018

2. Agenda
Templates
Model deployment flow
Sheep
Summary
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3. Templates

4. We have two types of
models

5. Models that are served as an API, so we must
provide Data Scientists some tools to train the
model and create a new micro-service
Online models

6. The kind of model whose runs are scheduled.
They take data from our data lake instead of
http request payloads
Batch models

7. Each type of model have their own requirements and
integrations with our different services.
Our templates structure provide Data Scientist with
the minimum set of tools they need to make the
model go to production. Plus, we provide some guide
lines to the development of a new model.

8. Model deployment flow

9. Deployment flow
This is a simple sketch of how we put models in production

10. Sheep

11. Can the deploy be an
automated process?

12. Maybe…

13. Can we make the Data
Scientist focus more on
the modelling stuff?

14. YES!

16. Sheep is our private python library to standardize
model deployment.
The idea behind it is to make the model deploy an
easy process, taking care of things like: security,
data gathering, service startup, model objects
management. It didn’t make everything automatic,
but it reduces a lot the amount of code the DS needs
to write

17. Sample code

18. Sample code

19. Sample code

20. Summary

21. Summary
A model is a code deliverable
too, so as you should already
know, write tests. You need to
know if everything is working
when a new PR is open.
Tests, tests, tests…
Even after the model is in
production you need to keep
an eye on it: how the features
behave, service metrics, and
model performance.
Be a Watchman
With standards we can
democratize the model code,
anyone familiar with the
standards should be able to
understand a new model.
Set standards