Keras

Keras is an open-source deep learning framework (machine learning frameworks) that provides a high-level, modular Application Programming Interface (API) for building, training, and deploying neural networks on multiple back-end runtimes.

• High-level Neural Network (NN) API for defining, training, and evaluating deep learning models (machine learning frameworks).
• Support for multiple execution backends and devices, including CPUs, GPUs, and other accelerators (compute frameworks).
• Predefined layers, models, metrics, losses, and optimizers for common deep learning workloads (model development tools).
• APIs for model saving, exporting, and deployment to various production environments (ML deployment).
• Integration with the broader Python scientific computing and Machine Learning (ML) ecosystem (developer tooling).

More About Keras

Keras is an open-source deep learning framework (machine learning frameworks) designed to provide a consistent, high-level interface for building and training neural networks. It focuses on enabling model definition through modular building blocks such as layers, models, optimizers, and losses, which can be composed to express a wide range of deep learning architectures. Keras operates as an abstraction layer over compatible backends, allowing users to target different hardware and runtime environments without changing high-level model code.

The core purpose of Keras is to streamline the creation and iteration of deep learning models (AI/ML development) through a clear, Python-based API. It offers components such as dense, convolutional, recurrent, attention, and normalization layers, along with utilities for input processing and model configuration. Keras provides model classes for both sequential and functional graph-style architectures, enabling straightforward linear stacks as well as more complex topologies with branching, merging, and shared layers. This architecture promotes reuse and consistency across different projects and teams.

Keras includes training and evaluation workflows (ML training pipelines) that wrap lower-level tensor operations with fit, evaluate, and predict routines. These support dataset iteration, batching, shuffling, callbacks, checkpointing, and metrics computation. The framework exposes configurable loss functions, metrics, and optimizers, including stochastic gradient-based methods, which can be attached to models without manual gradient handling. Model serialization features allow saving architectures, weights, and training configurations for later reuse, transfer, or deployment.

In enterprise and institutional environments (enterprise Artificial Intelligence (AI) platforms), Keras is used to develop models for tasks such as classification, regression, sequence modeling, and image, text, or tabular data processing. Because the API is independent from the underlying runtime, organizations can train on CPUs, GPUs, or specialized accelerators where supported by the chosen backend. This separation enables teams to standardize on Keras for model definition while aligning execution with available infrastructure and scaling strategies.

Keras interoperates with the broader Python ecosystem (developer tooling), integrating conceptually with data input pipelines, experiment tracking tools, and deployment stacks. Models authored in Keras can be exported and integrated into serving systems, embedded in applications, or converted for edge and mobile runtimes where supported by the underlying backend or associated tools. Extension points in Keras, such as custom layers, callbacks, and losses, allow teams to encode domain-specific logic while retaining the higher-level training and inference abstractions.

From a directory and taxonomy perspective, Keras is categorized as a deep learning framework (machine learning frameworks) and model development library (developer tooling). It sits at the application and model layer of AI infrastructure, above numeric compute runtimes and hardware accelerators, and is relevant for organizations standardizing their Machine Learning Operations (MLOps), experimentation, and production model lifecycle around a high-level NN API.