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Feast H2 Community Survey
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1. Which role best describes you?
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2. How many individuals in your organization are focused on machine learning problems (data scientists, data engineers, and ML engineers)?
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3. Our ML use cases are best described as
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4. How many models have been deployed and are being used in production in your organization (different use cases, not model versions)?
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5. We are running our production systems in the following environment(s)
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6. How important is having data quality monitoring/validation (Great Expectations) integration in Feast? (validating data being loaded into the feature store, and validating data being read from the feature store)
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7. How important is support for batch feature engineering in Feast? (being able to aggregate or transform batch/offline data into features using Feast, before storing the results in the feature store)
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8. How important is streaming feature engineering in Feast? (being able to aggregate or transform streaming data into features using Feast, before storing the results in the feature store)
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9. How important is support for on demand feature transformations in Feast (being able to transform or create features as a post-processing step after reading data from the feature store)?
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10. How would you prefer to integrate Feast into your model serving stack?
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11. How important is having a feature push/write API in Feast (provides the ability to write feature values to the feature store)?
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12. What are your latency requirements for serving online features in production (reading features from Feast)?
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13. How important is having support for streaming ingestion in Feast? (having Feast launch jobs that read feature data from a streaming data source and store it in the feature store)
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14. How important is having a Feast UI, with feature exploration, navigation, discovery?
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15. How do you want Feast to manage historical offline copies of production/online features (like streaming features/data)?
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16. I am able to use the following offline storage systems (for storing computed features and training data)
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17. I would like to use the following sources of data for online/production data
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18. I am able to use the following database(s) as my online feature store
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19. How important is it for Feast to support no-entity feature views (creating feature views that contain features that aren't associated with an entity)?
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20. How do you want feature view's to be versioned in Feast?
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21. How important is having first class embedding support in Feast?
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22. How important is having Ray support/integration in Feast? (either as a means of ingesting data into Feast, transforming data, or serving features)
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23. What would you like to see more of in our documentation?
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24. I feel confident using software written in the following programming languages in a production environment (high throughput and low latency serving)
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25. The main priority of the Feast contributors should be
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26. The second priority of the Feast contributors should be
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27. The main reason I am not using Feast is because
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28. What is the one capability that could be added or changed in Feast that would make the biggest impact to you?
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