A lot has happened last year with Feast
high-quality contributions:
high-performances feature serving
data quality and monitoring
feast UI
Which technology do you use for online feature store?
Why Postgres/MySQL? Simplicity?
Which technology for your offline feature store
Operational DBs present again, it is curious
Are operational DBs necessary for ML?
Operational DBs are shrinking, analytic stacks are increasingly being used for production ML
Is everyone using the modern data stack for production ML? What tools do you use for data transformation
Spark and pandas dominate, but these are not modern stack
Good representation of dbt with data warehouse
Is dbt enough to do ML?
dbt user always use another tool to transform data, important but not sufficient
Modern data stack is not yet enough for production ML, doesn't address yet:
Streaming, still the domain of Spark and Fling
On demand feature engineer, on the fly
How do you implement Feast for online feature retrieval
Surprising because python is slower than Java or Go, because understanding is easier
Users want to use Feast as a library, as opposed to deploying a new service
Even with a service, users still prefer Python, usability is the main argument
Why are teams adopting python in prod?
Team are optimizing for delivery over performance
Python is just writing another language (C/C++, Rust)
Python ML ecosystem is vast and growing rapidly