Designing a scalable and maintainable deep learning system architecture involves balancing multiple trade-offs. One major trade-off is the trade-off between flexibility and complexity.
On one hand, a flexible architecture should accommodate a wide range of model types and data sources. For example, it should allow for changes in data preprocessing, augmentation, and feature engineering. Keras is a highly flexible deep learning framework that enables users to build and train a wide variety of models using a range of network architectures and optimization algorithms. To ensure flexibility, the design should allow for modularization and easy component swapping.
However, flexibility can come at the cost of complexity. The more components you add to a system, the harder it generally becomes to maintain and scale. Therefore, we need to consider the complexity of maintaining the system. In order to alleviate complexity, one can opt for a simpler architecture where Keras is combined with other specialized and complementary components.
For example, data processing pipelines (such as Apache Beam or Apache Nifi) can be used to extract, transform, and load data into the system. These pipelines allow the data to be cleaned, transformed, and enriched before training the model. This approach could help to keep the data management and processing separate from the model training, which can be valuable in a number of use cases.
Another potential component to consider is the model serving infrastructure. After the model has been trained, it needs to be deployed to serve real-time predictions in production. For this purpose, there are specialized serving systems one can incorporate such as TensorFlow Serving or Seldon Core. These can handle the complexities of load-balancing, scaling, and versioning of the model.
In conclusion, a scalable and maintainable architecture that combines Keras with other components, such as data processing pipelines and model serving infrastructure, involves balancing the trade-offs between flexibility, complexity, and maintainability. By identifying a manageable number of specialized components that integrate easily with each other, we can establish a scalable and flexible pipeline for deep learning models that is easier to maintain over time.