Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by jamesblonde on Tuesday April 09, 2024 @03:57PM
(#39983565)
FeatureSets are mutable data and TrainingSets are consistent snapshots of feature data (from FeatureSets). I fail to see what that has to do with composability.
Join is still available for FeatureSets to enable composable feature views - join is resuse of feature data. GroupBy is just an aggregation in a feature pipeline, not sure your point here.
You can still do star schema (and even snowflake schema if you have the right abstractions).
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by jamesblonde on Tuesday April 09, 2024 @03:58PM
(#39983582)
Normalization is a model-dependent transformation and happens after the feature store - needs to be consistent between training and inference pipelines.
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by nikhilsimha on Tuesday April 09, 2024 @04:31PM
(#39983925)
Normalization is overloaded. I was referring to schema normalization (3NF etc) not feature normalization - like standard scaling etc.
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by jamesblonde on Tuesday April 09, 2024 @04:49PM
(#39984092)
Ok, but star schema is denormalized. Snowflake is normalized.
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by nikhilsimha on Tuesday April 09, 2024 @06:10PM
(#39984841)
To be pedantic, even in star schema - the dim tables are denormalized, fact tables are not.
I agree that my statement would be much better if used snowflake schema instead.
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by jvican on Tuesday April 09, 2024 @05:19PM
(#39984374)
Thank you for sharing!
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by throwaway2037 on Tuesday April 09, 2024 @11:04PM
(#39986566)
What is the meaning of pure kappa?
Re:Chronon, Airbnb's ML Feature Platform, Is Now Open Source
by pedrosorio on Tuesday April 09, 2024 @11:46PM
(#39986756)
Tecton, we evaluated, but decided that the time-travel strategy wasn’t scalable for our needs at the time.
A philosophical difference with tecton is that, we believe the compute primitives (aggregation and enrichment) need to be composable. We don’t have a FeatureSet or a TrainingSet for that reason - we instead have GroupBy and Join.
This enables chaining or composition to handle normalization (think 3NF) / star-schema in the warehouse.
Side benefit is that, non ml use-cases are able to leverage functionality within Chronon.