Machine Learning at CNN | Hacker News Reader

Machine Learning at CNN

medium.com

23 pointssavin-goyal10 comments

If this work is interesting, we're hiring on this team as well!

More specifically to this post, we are hiring an ML engineer to join this team- https://warnermediacareers.com/global/en/job/181319BR/Sr-Mac...

I'm the hiring manager and happy to answer any questions about the role. I just joined CNN after years at The Echo Nest/Spotify and we have some very exciting recsys work brewing.

We also have loads more jobs open in data intelligence, esp for product analysts- General CNN data intelligence job postings are here- https://warnermediacareers.com/global/en/search-results?keyw...

Our Data Intelligence team, in particular, leverages data and machine-learning capabilities to build innovative experiences for our audience and provides scalable solutions to CNN’s operations.

So, in plainer terms, what is the use that ML is being put to here? Recommending different stories to users? Choosing which reporters to send to which locations? What sort of stories get the most engagement? Which villain is trying to steal Aunt Em's ranch? Deep fake versions of their reporters when they're short on staff? Or What?

It seems like everyone with an IT department and lots of data is trying to do ML but the possibilities seem thinner and thinner. People look at this stuff have too easily said "AI Winter" but current ML useful enough that it won't go away but it still seems like some retrenchment will go.

Or, IDK, maybe there are a wider array of possible applications than I'm thinking of. I'm curious.

Just my experience from Netflix tells that a large company can have tens of use cases for optimization of different kinds, ML included.

Often it is the least hype-worthy cases that are the most valuable for the business (e.g churn prediction, marketing budget optimization etc)

At CNN our initial ML application is around personalization (ie article/video recommendations). While this is our main focus, we are exploring other applications. Eventually my hope is that we are able to provide tools that assist the content creators.

At the Navy, our best usage of ML is simply GUI scraping and figuring out the default action set for new hires paperwork. it basically is a screen macro that screams through forms. Took days before now takes 15 minutes. really dumb, but simple and useful in a bureaucratic 300k strong org, with possible real ML in the future for highlighting possible actions instead.

What were some of the pain points you face(d) - looking back at your Metaflow adoption? Disclaimer: I work in Netflix ML Platform that helped open-source Metaflow originally.

We've run into some issues with getting AWS Batch to play nicely, though I wouldn't say it is Metaflow specific. Initially we did quite a bit of troubleshooting the Stuck in RUNNABLE errors. We sometimes have issues with batch jobs that can't be satisfied by our compute environment causing other jobs in a queue to be blocked.

There are other small issues, but overall our ML engineers are very happy with it as a tool.

Interesting, thanks for sharing CNN! (Disclaimer: I hack Metaflow for living)

Do other folks find Terraform more appealing than Cloudformation in general?

my 2 cents.. I've done a decent amount of work in Cloudformation before working with Terraform, and I generally find it to be far more developer friendly to work with. It is easier to organize and refactor and the syntax is more readable. For me the only negatives are: 1) its not 1.x yet, so you have to pay attention to breaking changes between versions 2) remote state of the infrastructure isn't handled for you like in cloudformation. Neither of these have outweight the benefits for me.

So they use CNNs at CNN?

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