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 build simple AI applications that leverage pre-built ML models and allow you to interact with a UI to visualize the results.
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.
We often get asked how our feature store works with Microsoft Azure Cloud, so Nicholas Schenone put together this guide. This 4-part blog series will take you through the process step by step, with plenty of code examples and helpful tips.