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.
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.
The feature store has become a hot topic in machine learning circles in the last few months, and for good reason. It addresses the most painful challenge in the ML lifecycle: dealing with data, or in other words, feature engineering.
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.
2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable - business value.