This looks similar to Google's Mediapipe [0]
Netflix's Metaflow: Reproducible machine learning pipelines
51–60 of 105 posts
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#52If you are curious about how Netflix uses Metaflow to power behind-the-scenes machine learning, take a look at this recent blog article https://netflixtechblog.com/supporting-content-decision-make... Also I'm happy to answer any questions (I lead the Metaflow team at Netflix).
Hi Ville, thanks for coming on here to answer questions. I see that Metaflow has been made compatible with R now. Are there any plans to do the same with Julia?
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#53If you are curious about how Netflix uses Metaflow to power behind-the-scenes machine learning, take a look at this recent blog article https://netflixtechblog.com/supporting-content-decision-make... Also I'm happy to answer any questions (I lead the Metaflow team at Netflix).
Hi Ville, thanks for coming on here to answer questions. I see that Metaflow has been made compatible with R now. Are there any plans to do the same with Julia?
No plans but it should be possible technically. We'd need help from the Julia community :)
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#54Earlier quoted context omitted.
Do you have a counter example of a streaming service that does recommendations better?
It’s not a home run for me, but I think Spotify recommendations are quite good. They clearly use some form of content based recommendation (extracting features from the music itself) blended with other methods. It seems to make an honest attempt at serendipity (songs/artists you may like but would otherwise be unlikely to discover). I still think recommender engines should always enable some form of user tuning. If i…
Unless I’m missing this functionality somewhere (entirely possible), this lack of user-tuning has ruined Spotify recs for me, since I listen to entirely different playlists when working or meditating. I don’t check out recommendations to get the latest binaural beats or nature sounds, you know?
Although even before I started listening to Spotify while working etc, it seemed to have run out of things to recommend. My weekly discover playlist would be half things I’d already liked. So...who knows. But I miss the discovery functionality quite a bit.
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#55Earlier quoted context omitted.
Hey, been meaning to reach out. There's a bit in the Metaflow docs that talks about choosing resources, like RAM: "as a good measure, don't request more resources than what your workflow actually needs. On the other hand, never optimize resources prematurely." The problem is that for memory, too little means out-of-memory crashes, so the tendency I've seen is to over-provision memory, which ends up getting very expen…
The Spark community (LinkedIn) developed Dr Elephant to profile jobs and provide suggestions for reducing memory/cpu consumption. Metaflow would need something similar: https://github.com/linkedin/dr-elephant
I've seen that, yeah. I've already implemented a memory profiler for Python batch jobs (https://pythonspeed.com/fil), but starting to think about how to integrate it into specific pipeline frameworks.
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#56Earlier quoted context omitted.
Do you have a counter example of a streaming service that does recommendations better?
It’s not a home run for me, but I think Spotify recommendations are quite good. They clearly use some form of content based recommendation (extracting features from the music itself) blended with other methods. It seems to make an honest attempt at serendipity (songs/artists you may like but would otherwise be unlikely to discover). I still think recommender engines should always enable some form of user tuning. If i…
I like Spotify's recommendations and have found lots of great artists from it. But now I feel I'm in a kind of Spotify-created rut of listening to static groups of artists. I have mostly stopped listening to my daily mixes because of this.
It's also easy to theorize about "big media" controlling what I listen to, so I still feel the need to do my own exploring, even when I'm getting recommended good fresh stuff.
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#57How does this compare against TensorFlow Extended (TFX)? https://www.tensorflow.org/tfx
Metaflow was built to assist in both developing ML models and deploying/managing them in production. AFAIK, TFX is focused on the deployment story of ML pipelines. https://docs.metaflow.org/introduction/what-is-metaflow#shou...
It's focused on building ML pipelines (similar to what Cortex aims to be).
In addition, it also conveniently supports integration with Orchestrators like Airflow, Kubeflow, Beam etc. This book "Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow" (https://www.amazon.com/dp/1492053198/ref=cm_sw_em_r_mt_dp_Ig...) goes into great details.
I was curious to see what advantage Metaflow offered over TFX.
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#58Earlier quoted context omitted.
It's not just that (obviously, since It's not even possible for me to rate a movie more granular than thumbs up/down) What happened is that the notion changed from "predict a scalar rating" to "predict a binary satisfaction." As parent poster noted, the effect of this is to push "3 star" and "4 star" acceptable shows to the user, instead of "5 star" great (in the user's view) shows. Also, in Netflix's defense, users…
This was a sad turning point for me. We used to have a single streaming platform with an awesome library, a granular review system, and user reviews. You could easily take a quick look to read other users thoughts on a film. Now I have to lookup metacritic / reviews myself, and the NF recommendation is based on whether I've said something is 'palatable enough to watch, isn't terrible, but I'd never watch it again' (t…
I've wondered if the lack of this stuff is due to business contracts or internal product goals. Not having e.g. IMDB makes sense since it is owned by a competitor (Amazon; whether that's a USA Trust thing, who knows)
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#59Edit: this is a somewhat OT rant. Netflix’s recommender system is hands down the worst I have ever seen. Every single thing I watch, it suggests The Queen’s Gambit and two other random Netflix productions. Even if I watch the first of a trilogy (LotR, for example). How can they be so terrible at this? The categories in the main browsing view are also hysterically arbitrary. It kind of looks like a topic model with ba…
> recommender system is hands down the worst I have ever seen.
In my limited experience, they are all dismal and best ignored entirely.
When it was easier to see something plausibly like a real user review, that helped I guess.
Re: Netflix's Metaflow: Reproducible machine learning pipelines
#60Is there a comparison with TFX, KubeFlow pipelines and AirFlow?