January 11, 2018
We're partnering with FusionCell to survey folks about how they monitor machine learning learning applications. FusionCell's Robert Dempsey joins us today to share some of the personal pain he's faced monitoring machine learning apps. Robert has been helping companies and engineers learn about, build and leverage machine learning systems.
When StackOverflow surveyed 64,227 software developers from 213 countries, Python came out as the programming language most wanted by employers.
Machine learning, or "ML" for short.
Over the past few years Python has taken over in machine learning thanks to libraries like Pandas and Scikit-learn.
But everything needs time to mature.
When it comes to monitoring machine learning systems, how to monitor them currently consists of gluing together many pieces of tech.
Is there a better way to hunt down and eradicate the bottlenecks in your ML systems?
There may be. But we need your help to do so.
I probably don't have to sell you on why monitoring production applications is important, but just for fun let's look at three typical scenarios where it really helps:
With a machine learning system it can be a tricky proposition because of all the moving parts...
Every data pipeline I've ever worked on is some variation of this:
All that tech brings along some fun challenges...
Even the simplest machine learning systems consist of many moving parts. The most basic I've built was deployed to a single server and consisted of:
One of the largest I've worked on consisted of:
In all cases we had monitoring in place.
But here's the rub - the metrics we used were surface level.
When I had to look at the performance of my code I had to either add timing information to my code and output it to the logs, or profile it locally.
Both of these solutions were non-optimal.
First I had to roll my own metrics. While I was working with a team and we did code reviews, we didn't always have the luxury of being able to have a considered discussion of what we should be measuring. That means I had to come up with the metrics I thought were best. And while I would love to sit here and tell you my code performs like a champ at all times, that ain't reality, for any of us.
Second, while I develop on a Mac and deploy to Linux, there are other variables in play that could affect my application. How many times has something worked awesome locally only to perform not as well in production? Right. Profiling my code locally can help, but it won't tell me how my code performs in a production environment.
Third, I lacked the full picture. What I was doing was combining surface-level metrics with metrics of the environment my app is running in. That led to some seriously complex dashboards. Our brains are good, but try staring at some 10+ pane dashboard with metrics streaming in and see how long it takes before you go cross-eyed.
These challenges are present in all Python applications I've encountered.
And that leads to my question - how are you monitoring your Python applications?
I've teamed up with the awesome team at Scout to find out, and we need your help!
Today we're launching our Python Monitoring Survey. You can take the survey here.
The survey will be open from today through the middle of February. The results will be published on this blog in the beginning of March.
Just add your email address at the end of the survey and we'll give you first access to the survey results.
Click here to take the survey today.