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So, every month or two I see another article tut-tutting people for putting notebooks into production, and I'm curious, who is actually doing this? I've never s

So, every month or two I see another article tut-tutting people for putting notebooks into production, and I'm curious, who is actually doing this? I've never seen such a thing in the wild, and I'm genuinely (morbidly?) curious what it would look like in practice.

ipunchghosts 7y ago
Me too! I find notebooks are only good for teaching. No matter how hard I try, they never help me when doing my own analysis!
bart_spoon 7y ago
Netflix apparently. They've built an entire software framework for enabling their data scientists to put their notebooks into production [0]. [0] https://netflixtechblog.com/notebook-innovation-591ee3221233 https://netflixtechblog.com/notebook-innovation-591ee3221233
- vtuulos 7y ago
  We (Netflix) do a ton of prototyping/exploration in notebooks like everyone else. We run many ETL pipelines in production as templated notebooks. When something fails, you can just open a notebook to see the input and the output, which is handy. We don't deploy or execute ML models in production as notebooks. We have many other solutions for that use case. In particular, check out https://metaflow.org https://metaflow.org
autokad 7y ago
with databricks, its very easy to put a notebook into production. I've done it several times
gowld 7y ago
A notebook is just a script that runs in a fancy UI. Have you never seen a script in production?
musingsole 7y ago
I just completed a project using notebooks in production. Granted, it was as a stepping stone to a more traditional application, but for 3 months, our production system was readily executing notebooks and consuming their output. Papermill (https://papermill.readthedocs.io/en/latest/ https://papermill.readthedocs.io/en/latest/) made it extremely easy, to the point where I question the real value of moving away from this model. But software engineers practically hiss when you mention Jupyter because it's too different from the rest of their tooling.
mateo411 7y ago
If you use Databricks, it's pretty easy to schedule a Databricks notebook to run. AWS EMR also has notebook support, I haven't used it, but it might be possible to schedule those too. You would still have to grant permission to the Databricks role to write to a production location in S3. I really dislike this practice. Code that runs in production should be code reviewed and there should be some monitoring in place to make sure the job is working correctly

ipunchghosts 7y ago

Me too! I find notebooks are only good for teaching. No matter how hard I try, they never help me when doing my own analysis!

bart_spoon 7y ago

Netflix apparently. They've built an entire software framework for enabling their data scientists to put their notebooks into production [0]. [0] https://netflixtechblog.com/notebook-innovation-591ee3221233 https://netflixtechblog.com/notebook-innovation-591ee3221233

vtuulos 7y ago
We (Netflix) do a ton of prototyping/exploration in notebooks like everyone else. We run many ETL pipelines in production as templated notebooks. When something fails, you can just open a notebook to see the input and the output, which is handy. We don't deploy or execute ML models in production as notebooks. We have many other solutions for that use case. In particular, check out https://metaflow.org https://metaflow.org

autokad 7y ago

with databricks, its very easy to put a notebook into production. I've done it several times

gowld 7y ago

A notebook is just a script that runs in a fancy UI. Have you never seen a script in production?

musingsole 7y ago

I just completed a project using notebooks in production. Granted, it was as a stepping stone to a more traditional application, but for 3 months, our production system was readily executing notebooks and consuming their output. Papermill (https://papermill.readthedocs.io/en/latest/ https://papermill.readthedocs.io/en/latest/) made it extremely easy, to the point where I question the real value of moving away from this model. But software engineers practically hiss when you mention Jupyter because it's too different from the rest of their tooling.

mateo411 7y ago

If you use Databricks, it's pretty easy to schedule a Databricks notebook to run. AWS EMR also has notebook support, I haven't used it, but it might be possible to schedule those too. You would still have to grant permission to the Databricks role to write to a production location in S3. I really dislike this practice. Code that runs in production should be code reviewed and there should be some monitoring in place to make sure the job is working correctly