SageMaker Fridays Season 3, Episode 3 — Managing engineered features with SageMaker Feature Store
In this episode, we build a sentiment analysis model starting from the Amazon Customer Reviews dataset. First, we import the dataset in Parquet format in Amazon Athena. Then, we import it from Athena to SageMaker Data Wrangler for a quick look. Then, we move to a Jupyter notebook and we start engineering features using popular open source libraries (nltk and spaCy), and we automate them with SageMaker Processing. Next, we load the processed dataset in SageMaker Feature Store, both offline and online. Next, we run Athena queries on the offline store in order to build a training set, which we use to train and deploy a sentiment analysis model with the built-in BlazingText algorithm. Finally, we see how to update and delete individual features in the online store, and how to use timestamps for feature versioning. 100% live, no slides :)
