Add Feast overview to README by woop · Pull Request #8 · feast-dev/feast
## High Level Architecture
The Feast platform is broken down into the following functional areas:
* __Create__ features based on defined format and programming model * __Ingest__ features via streaming input, import from files or BigQuery tables, and write to an appropriate data store * __Store__ feature data for both serving and training purposes based on feature access patterns * __Access__ features for training and serving * __Discover__ information about entities and features stored and served by Feast
## Motivation
__Access to features in serving__: Machine learning models typically require access to features created in both batch pipelines, and real time streams. Feast provides a means for accessing these features in a serving environment, at low latency and high load.
__Consistency between training and serving__: In many machine learning systems there exists a disconnect between features that are created in batch pipelines for the training of a model, and ones that are created from streams for the serving of real-time features. By centralizing the ingestion of features, Feast provides a consistent view of both batch and real-time features, in both training and serving.
__Infrastructure management__: Feast abstracts away much of the engineering overhead associated with managing data infrastructure. It handles the ingestion, storage, and serving of large amount of feature data in a scalable way. The system configures data models based on your registered feature specifications, and ensures that you always have a consistent view of features in both your historical and real-time data stores.
__Feature standardisation__: Feast presents a centralized platform on which teams can register features in a standardized way using specifications. This provides structure to the way features are defined and allows teams to reference features in discussions with a singly understood link.
__Discovery__: Feast allows users to easily explore and discover features and their associated information. This allows for a deeper understanding of features and theirs specifications, more feature reuse between teams and projects, and faster experimentation. Each new ML project can leverage features that have been created by prior teams, which compounds an organization's ability to discover new insights.
## More Information
* [Components](docs/components.md) * [Concepts](docs/concepts.md)
## Notice
Feast is still under active development. Your feedback and contributions are important to us.
## Source Code Headers
Every file containing source code must include copyright and license