Distributed ingestion is a great way to increase scalability for ML use cases with large datasets. But like any ML component, integrating and maintaining another tool introduces engineering complexity. Here's how to simplify it.
Distributed ingestion is a great way to increase scalability for ML use cases with large datasets. But like any ML component, integrating and maintaining another tool introduces engineering complexity. Here's how to simplify it.
Here's how to use the Iguazio feature store to build, store and share features from your Snowflake data.
Feature stores enable data scientists to reuse features instead of rebuilding these features again and again for different models, saving them valuable time and effort.
In part 4 of the guide to using Azure ML with the Iguazio feature store, we cover model serving on-prem and hybrid cloud, and model monitoring.
In part 3 of the guide to using Azure ML with the Iguazio feature store, we cover model training via Azure ML, while leveraging Iguazio.
In part 2 of the guide to using Azure ML with the Iguazio feature store, we cover data ingestion and transformation into the feature store.