The team has been hard at work to get out v0.7 of Featureform! This includes a whole new API to define features, labels, and entities based on tons of users feedback. It wouldn't have been possible without you all. It also includes on-demand features which allows users to transform data they receive at serving time. This opens a wide variety of computer vision and NLP (including embeddings) oriented workflows.
We also want to give a big shout out to our community members and our customers for their ongoing support and feedback. We look forward to hearing your thoughts on v0.7. Feel free to drop us a line in our Slack community.
Featureform has added a new way to define entities, features, and labels. This new API, which takes inspiration from Python ORMs, makes it easier for data scientists to define and manage their features and labels in code.
Example
You can read more in the docs.
A highly requested feature was to feature-ize incoming data at serving time. For example, you may have an on-demand feature that turns a user comment into an embedding, or one that processes an incoming image.
On-demand feature that turns a comment to an embedding at serving time
You can learn more in the docs
All features, labels, transformations, and training sets now have a tags and properties argument. properties is a dict and tags is a list.
You can read more in the docs.
Featureform has a local mode that allows users to define, manage, and serve their features when working locally off their laptop. It doesn’t require anything to be deployed. It would historically re-generate training sets and features on each run, but with 0.7, we cache results by default to decrease iteration time.
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See what a virtual feature store means for your organization.
