Machine Learning News

Tecton now natively supports Array type features with Float32, Float64, Int64, and String type elements.

Tecton is releasing a new Run API to be used for dry-run executing a FeatureView's transformation the same way Tecton will execute it during materialization or at feature retrieval time.

cton is releasing a new API to be used for feature retrieval. For fetching features from the offline store, we've introduced the method get_historical_features. For fetching features from the online store, we've introduced the method get_online_features.

You can now include batch_cluster_config configuration in Feature Table declarations. By specifying an EMRClusterConfig or DatabricksClusterConfig configuration or referencing an ExistingClusterConfig, the ingest materialization jobs will run on a cluster of workers with the specified configuration options like instance type, instance size, and spark configurations.

Existing admin users can now directly grant admin privileges to other users through the Admin console, rather than having to make a request to Tecton support. See the Admin console documentation for more detail.

You can now create additional workspaces with automatic materialization by using the flag --live.

The CLI "API key" commands have been refactored as sub-commands for consistency and clearer help docs.

In addition to monitoring the materialization status for individual Feature Views, we've now added a summary dashboard to easily see if any of the Feature Views in a workspace are stale or have failing materialization jobs.

The Tecton SDK is now being published to PyPI at https://pypi.org/project/tecton/ for easier installation.

Continuous mode for Stream Window Aggregate Feature Views now enables new event data to be included in feature values in less than a second.