Hi Folks! We're excited to announce V0.9 of Featureform, one of our biggest releases to date! It includes:
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 v0.9. Feel free to drop us a line in our Slack community.
You can use Featureform to define and orchestrate data pipelines that generate embeddings. Featureform can write them into either Redis for nearest neighbor lookup. This also allows users to version, re-use, and manage embeddings declaratively. Learn more about Redis' Vector Store and Vector Similarity Search here.
You can register Redis as a vector store using the same method as you would for a non-Vector Store provider.
Featureform now allows you to generate embeddings from text using OpenAI's Embeddings. Simply add your
You can store your embedding definitions and version them with a Featureform variant as you iterate. Use the "embed_docs" tuple to specify the entity ID and the column name in index 0 and 1, respectively, and the "variant" parameter to specify a version.
In v0.8, we added the ability to interact with sources as dataframes. Prior to v0.8, users couldn't experiment with data they had previously registered with a provider. Instead of registering transformations and working with them during experimentation, they had to wait until they were production ready to register them with Featureform. Our new experimental api removes this workflow disruption and allows data scientists to serve data directly from sources registered on providers. This eliminates the need for separate connections or additional database client libraries, streamlining the data experimentation process and allowing users to register transformations more efficiently.
We're excited to extend the same functionality to training sets as well! In this example, you can specify the name ("fraud") of the training set and variant (ex. "simple").
Featureform supports Cron syntax for scheduling transformations to run. This release rebuffs this functionality to make it more stable and efficient, and also adds more verbose error messages.
A transformation that runs every hour on Snowflake
Featureform schedules and runs your transformations for you. We support running Pandas directly, Featureform spins up a Kubernetes job to run it. This isn’t a replacement for distributed processing frameworks like Spark (which we also support), but it’s a great option for teams that are already using Pandas for production.
From overviews to niche applications and everything in between, explore current discussion and commentary on feature management.