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-images.s3.amazonaws.com/shared/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: '2026-05-19'
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-images.s3.amazonaws.com/shared/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-images.s3.amazonaws.com/shared/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-images.s3.amazonaws.com/shared/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: LinkedIn
    url: https://www.linkedin.com/showcase/tensorflowdev
  - 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: StatusPage
    url: https://github.com/tensorflow/tensorflow
  - type: JSONLD
    url: json-ld/google-tensorflow-context.jsonld
maintainers:
  - FN: Kin Lane
    email: kin@apievangelist.com