Imagine you're Spotify and you have a stream of user-song-timestamp triplets per listen. You'll likely want to transform it into features such as: top genre per user in last 30 days. As a data scientist, you'll write your transformations to do so and run it yourself on something like Spark and store it on Redis for inference and S3 for training. You have to keep track of your versioning, jobs, and transformations. You also can't easily share them across data scientists.
Featureform's library allows you to define your transformations, feature, and training sets. It will interface with Spark, Redis, etc. on your behalf to achieve your desired state. It'll also keep track of all the metadata for you and easily make it share-able and re-usable.
simba-k|3 years ago
Featureform's library allows you to define your transformations, feature, and training sets. It will interface with Spark, Redis, etc. on your behalf to achieve your desired state. It'll also keep track of all the metadata for you and easily make it share-able and re-usable.