
Visibility and Monitoring for Machine Learning Models - heitortsergent
http://blog.launchdarkly.com/visibility-and-monitoring-for-machine-learning-models/
======
mav3r1ck
_“The tricky thing, though, is in order to get good at machine learning, you
need to be able to do deploys as fast as humanly possible and repeatedly as
humanly possible. Deploying a machine learning model isn’t like deploying a
regular code patch or something like that, even if you have a continuous
deployment system.” -Josh_

Sounds a lot more like a DevOps problem then a Machine Learning problem to
me+. But really, in general, this is something any one doing any sort of
software deployment should be doing to begin with. If I encountered a
continuous deployment system that doesn't already do this, then I usually take
the time to get it as close as possible. Still haven't gotten anywhere close
to Netflix's level, maybe some day.

\+ This is the buzziest comment I have probably ever made.

------
infinitone
Apart from the occasional pun or allegorical comment... frankly that
presentation ended very abruptly with very little actual substance. Or maybe i
was just expecting more?

------
mkagenius
Not sure I get the context or how slack operates -- but wouldn't most of the
big companies have functional tests already in place to catch erratic ML
models in production. If they are deploying ML model for the first time for
some new features, wouldn't they require to test the functionality and SLAs..

------
joe_the_user
This looks very interesting in terms of addressing machine learning as
application, which is to say that ordinary software has both the property
"does it's job" and "can be looked-at, tested and so-forth to see how it does
it's job" with machine learning systems as nominally defined having only the
first property.

Edit: I originally asked for transcript, it is just video.

~~~
wellactually
The transcript is below the video, on the same page.

~~~
joe_the_user
Just saw that. I'm so used to no transcript, I didn't look.

