content: #  Copyright (c) ZenML GmbH 2021. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
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  """A feature store enables an offline and online serving of feature data.
  
  Feature stores allow data teams to serve data via an offline store and an online
  low-latency store where data is kept in sync between the two. It also offers a
  centralized registry where features (and feature schemas) are stored for use
  within a team or wider organization.
  
  As a data scientist working on training your model, your requirements for how
  you access your batch / 'offline' data will almost certainly be different from
  how you access that data as part of a real-time or online inference setting.
  Feast solves the problem of developing train-serve skew where those two sources
  of data diverge from each other.
  """
  from zenml.feature_stores.base_feature_store import BaseFeatureStore
  
  __all__ = ["BaseFeatureStore"]
  