It's often said that the hardest part of MLOps is building and maintaining your datasets. This talk covers the key abstraction of the Dataframe and why Dataframes are such powerful abstractions for this critical part of your MLOps workflow.
We use Daft (www.getdaft.io) as a running example of a Dataframe to showcase how flexible this interface really is for heavy complex data processing, analytics, I/O and feeding your machine learning training pipelines.
It's often said that the hardest part of MLOps is building and maintaining your datasets. This talk covers the key abstraction of the Dataframe and why Dataframes are such powerful abstractions for this critical part of your MLOps workflow.
We use Daft (www.getdaft.io) as a running example of a Dataframe to showcase how flexible this interface really is for heavy complex data processing, analytics, I/O and feeding your machine learning training pipelines.