tl; dr; A combinator data component that installs Feast, a feature store.
Feast is an open-source feature store. A feature store allows you to manage, govern, and trace features derived from raw data. This is useful because it helps to unify and standardise, which reduces waste, improves quality, and makes models more reproducible.
Feast does not perform any computation. You can think of it as a meta-database; a database that manages other databases. It effectively creates a cache of feature data, keyed by time. The Feast libraries and CLIs provide a consistent way of pushing or streaming new data into the cache. Downstream systems use a similar interface to access point-in-time data. Learn more about feast in the documentation.
The fastest way to get started is to use the test drive functionality provided by TestFaster. Click on the "Launch Test Drive" button below (opens a new window).
Once the component has launched, click on the Jupyter link. Feast does not come with a UI. You will use Jupyter to interact with Feast via its API.
Once inside Jupyter, browse to the minimal notebook, which is the official example. Follow the instructions in the notebook.
Start by preparing your Kubernetes cluster using one of the infrastructure components or use your own cluster.
module "feast" {
source = "combinator-ml/feast/k8s"
# Optional settings go here
}See the full configuration options below.
No requirements.
| Name | Version |
|---|---|
| helm | n/a |
| kubernetes | n/a |
| random | n/a |
No Modules.
| Name |
|---|
| helm_release |
| kubernetes_namespace |
| kubernetes_secret |
| random_password |
| Name | Description | Type | Default | Required |
|---|---|---|---|---|
| name_prefix | Prefix to be used when naming the different components of Feast | string |
"combinator" |
no |
| namespace | (Optional) The namespace to install into. Defaults to feast. | string |
"feast" |
no |
No output.