Google TensorFlow

aid: google-tensorflow
name: Google TensorFlow
description: Google TensorFlow is an open-source machine learning framework providing APIs and tools for building, training, and deploying ML models, including TensorFlow Serving for model inference and TensorFlow Hub for reusable model components.
image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg
url: https://raw.githubusercontent.com/api-evangelist/google-tensorflow/refs/heads/main/apis.yml
created: '2026-03-13'
modified: '2026-04-28'
specificationVersion: '0.19'
type: Index
tags:
  - AI
  - Deep Learning
  - Google
  - Machine Learning
  - Model Serving
  - Open Source
apis:
  - name: TensorFlow Serving REST API
    description: >-
      TensorFlow Serving provides a REST API for serving trained TensorFlow models
      in production environments. The API supports model prediction (inference),
      classification, and regression requests against deployed models. It allows
      specifying model names and versions, and returns predictions in JSON format.
      TensorFlow Serving handles model lifecycle management, versioning, and
      concurrent request processing.
    image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg
    humanURL: https://www.tensorflow.org/tfx/serving/api_rest
    baseURL: http://localhost:8501
    tags:
      - Inference
      - Model Serving
      - Predictions
    properties:
      - type: Documentation
        url: https://www.tensorflow.org/tfx/serving/api_rest
      - type: OpenAPI
        url: openapi/tensorflow-serving-openapi.yml
      - type: JSONSchema
        url: json-schema/google-tensorflow-predict-request-schema.json
  - name: TensorFlow Hub API
    description: >-
      TensorFlow Hub provides a repository of reusable trained machine learning
      models. The API allows developers to search, discover, and download pre-trained
      models and model components (SavedModels, TF.js models, TFLite models) that
      can be reused for transfer learning and inference in new applications.
    image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg
    humanURL: https://tfhub.dev
    baseURL: https://tfhub.dev
    tags:
      - Models
      - Pre-Trained Models
      - Transfer Learning
    properties:
      - type: Documentation
        url: https://www.tensorflow.org/hub
  - name: TensorFlow Model Analysis API
    description: >-
      TensorFlow Model Analysis (TFMA) provides tools and APIs for evaluating
      TensorFlow models. It enables computing metrics over large datasets using
      Apache Beam, slicing evaluation results across different features, and
      tracking model performance over time for validation and monitoring purposes.
    image: https://kinlane-productions2.s3.amazonaws.com/apis-json/apis-json-logo.jpg
    humanURL: https://www.tensorflow.org/tfx/model_analysis/get_started
    baseURL: https://localhost
    tags:
      - Analysis
      - Metrics
      - Model Evaluation
    properties:
      - type: Documentation
        url: https://www.tensorflow.org/tfx/model_analysis/get_started
common:
  - type: GettingStarted
    url: https://www.tensorflow.org/learn
  - type: Pricing
    url: https://www.tensorflow.org
  - type: SDKs
    url: https://www.tensorflow.org/install
  - type: Support
    url: https://www.tensorflow.org/community
  - type: Status
    url: https://github.com/tensorflow/tensorflow
  - type: JSON-LD
    url: json-ld/google-tensorflow-context.jsonld
maintainers:
  - FN: Kin Lane
    email: [email protected]