Backend
A backend is a software component or subsystem that provides underlying processing, data access, execution, or services to clients or other components, typically behind a user-facing interface.
Rust Servers, Services, and Apps
A backend service is a server-side application component that implements application functionality and exposes it to clients or other services.
- How it is used
- Places server-side application logic between client frontends and distributed infrastructure through exposed APIs.
- How deep it goes
- Defines responsibilities, requirements, integrations, and deployment forms but stops short of implementation details.
- Examples
- Uses a social media application's distributed stack and process- or container-based backend deployments.
- What you can do after
- Evaluate backend designs against API quality, throughput, latency, availability, and resource-footprint requirements.
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Deep Learning with Python, Third Edition
The underlying execution framework that a machine-learning library can target to run models.
- How it is used
- Runs Keras models through a selectable tensor-computing framework and connects them to hardware and deployment ecosystems.
- How deep it goes
- Explains backend selection, portability, tensor operations, and gradients, while stopping short of framework-specific implementation details.
- Examples
- Uses KERAS_BACKEND, ~/.keras/keras.json, JAX selection, and NumPy’s prediction-only limitation as concrete examples.
- What you can do after
- Operate/configure Keras by selecting a backend before import and matching it to debugging, training, or inference needs.
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Building User-Friendly DSLs
The server-side part of a software system that provides services, storage, and processing for its frontend.
- How it is used
- Supports a Domain IDE by exposing an API, storing DSL content, and enabling frontend persistence and Runtime generation.
- How deep it goes
- Implements a deliberately simple REST backend with JSON-file persistence, serialization, deserialization, and reference handling.
- Examples
- Uses a JSON file on disk with GET retrieval, PUT replacement, and AST reconstruction through deserialization.
- What you can do after
- Build a simple REST backend that persists, transports, and reconstructs an AST for a frontend client.
Deep Learning with R, Third Edition
A backend engine is a software system that executes the numerical and machine-learning operations required by a higher-level framework.
- How it is used
- Runs Keras models by supplying low-level tensor computation, gradients, and hardware execution beneath its high-level API.
- How deep it goes
- Defines backend responsibilities and switching, then gives R configuration paths; stops short of implementing tensor operations or gradient internals.
- Examples
- Uses JAX selection in R, CPU/GPU backend suffixes, and workflows split across debugging, training, and inference.
- What you can do after
- Operate/configure Keras by selecting a backend with use_backend() or KERAS_BACKEND before loading keras3.
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Related concepts
- web applicationpart of · A web application is software that provides functionality through web technologies and is accessed by users through a browser or other web client.
- databaseuses · A database is an organized collection of persistent data managed by software for storage, retrieval, querying, and modification.
- natural language interfaceused by · A natural language interface enables interaction with a system through human-language text or speech.
- frontend developmentcontrasts with · Frontend development is the practice of building the user-facing, client-side interfaces and behavior through which people interact with software.
- JAXincludes · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
- contractuses · An API contract is a specification of the operations, data, and behavioral expectations that govern interactions between software components.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.