41–50 of 105 posts
Kedro has the simplest, leanest, functional-programming inspired pipeline definition and also spits out AirFlow and other formats readily + comes in with an integrated visualisation framework which is stunning & effective.
Edit: 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…
But the recommendations seem to work perfectly for me - I wonder whether that is the case for the silent majority?
The match % is usually spot on for me, and I've never seen it recommend any titles I've given a thumbs-down to.
If 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).
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…
Earlier quoted context omitted.
>Finally, they replaced the ratings with “% matching”. I guess so they can recommend their subpar productions even if they get low ratings. That's always what ratings were. People didn't understand that (as you can see), so they changed it to make it more transparent. https://www.businessinsider.com/why-netflix-replaced-its-5-s... >Netflix’s star ratings were personalized, and had been from the start. That means when…
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…
I remember they stated the reviews would still be available in some form for export.
It's no longer the Netflix of old imo.
How does this compare against TensorFlow Extended (TFX)? https://www.tensorflow.org/tfx
https://docs.metaflow.org/introduction/what-is-metaflow#shou...
Setting up a decent, comprehensive, self-hosted (!) ML environment is still extremely, frustratingly difficult. What I really want is a single solution, or a set of pluggable, integrated components that offer: * training data and model storage (on top of a blob store like S3, minio, ...) * interactive dev environments (Notebooks, dev containers, ...) * training (with history, comparisons, parameters, ...) with experi…
Open-source:
* https://github.com/logicalclocks/hopsworks
Managed platform on AWS/Azure (with elastic compute/storage, integration with managed K8s, LDAP/AD):
Edit: 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…
I long ago ceased to believe that the Netflix recommender system serves any other purpose than to fulfill the company's internal obligations to push favored content, depending on what it cost. Sadly, the same is now true for Amazon Prime, which is an even hotter mess.
Setting up a decent, comprehensive, self-hosted (!) ML environment is still extremely, frustratingly difficult. What I really want is a single solution, or a set of pluggable, integrated components that offer: * training data and model storage (on top of a blob store like S3, minio, ...) * interactive dev environments (Notebooks, dev containers, ...) * training (with history, comparisons, parameters, ...) with experi…
But unfortunately, as you say, most of the pluggable tools are not very good and/or not mature enough.
Here's our attempt at model storage, experiment tracking, and software heritage: https://replicate.ai/
For interactive dev environments, Colab, Deepnote, and Streamlit are all great.
For deploying to production, Cortex mentioned in the post is great.
All are a work in progress, but I think we'll soon have a really powerful ecosystem of tools.
Edit: 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…
Do you have a counter example of a streaming service that does recommendations better?
Not a streaming service but Google's Discover news is also very good (probably the best recommendations I have come across).
If 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).
What is Metaflow's explicit support for Transfer Learning tasks? In other words, how do I know what models to use or not use? I am surmising from the techblog post that there is a stable set of content-intrinsic features, and that can be separated from perhaps more dynamic features-sets that characterize audiences, presentation treatment, and viewing (as conditioned on all the other stuff). But it sounds like there i…
Metaflow is rather unopinionated about those types of questions, since they are subject to active research and experimentation. Metaflow aims to make it easy to conduct the research and experiments but it is up to the data scientist to choose the right modeling approach, features etc.
In some cases, individual teams have built a thin layer of tooling on top of Metaflow to support specific problems they care about. I could imagine such a layer for specific instances of transfer learning, for instance.
In general, we are actively thinking if/how Metaflow could support feature sharing in general. It is a tough nut to crack.