AWS SageMaker Feature Store (SMFS) Platform Service
A AWS SageMaker Feature Store (SMFS) Platform Service is an ML feature store platform that is an AWS service.
- Context:
- It can have an SMFS Offline Feature Store.
- It can have an SMFS Ingestion API.
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- Example(s):
- Counter-Example(s):
- See: AWS SageMaker.
References
2021
- https://towardsdatascience.com/ingesting-historical-feature-data-into-sagemaker-feature-store-5618e41a11e6
- QUOTE: ... Some of these customers have historical feature data they would like to migrate to the SMFS offline store which can store large volumes of feature data that is used to keep track of historical feature values and to create train/test data for model development or by batch applications.
A major challenge when ingesting historical data into SMFS offline store is that users will get charged when using the ingestion APIs, even if they only want to backfill historical data. These charges can grow quickly if customers have Terabytes of data they want to migrate to SMFS. …
- QUOTE: ... Some of these customers have historical feature data they would like to migrate to the SMFS offline store which can store large volumes of feature data that is used to keep track of historical feature values and to create train/test data for model development or by batch applications.
2020
- https://aws.amazon.com/sagemaker/feature-store/
- QUOTE: Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, retrieve, and share machine learning (ML) features.
Features are the attributes or properties models use during training and inference to make predictions. For example, in a ML application that recommends a music playlist, features could include song ratings, which songs were listened to previously, and how long songs were listened to. The accuracy of a ML model is based on a precise set and composition of features. Often, these features are used repeatedly by multiple teams training multiple models. And whichever feature set was used to train the model needs to be available to make real-time predictions (inference). Keeping a single source of features that is consistent and up-to-date across these different access patterns is a challenge as most organizations keep two different feature stores, one for training and one for inference.
Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (e.g. feature name or version number) so that you can query the features for the right attributes in batches or in real time using Amazon Athena, an interactive query service. SageMaker Feature Store Platform also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference.
- QUOTE: Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, retrieve, and share machine learning (ML) features.