在機器學習的相關工作中，資料可說是最重要的一項因素。 TensorFlow 提供多項資料工具，可協助你大規模整合、清除及預先處理資料：
Standard datasets for initial training and validation
Highly scalable data pipelines for loading data
Preprocessing layers for common input transformations
Additionally, responsible AI tools help you uncover and eliminate bias in your data to produce fair, ethical outcomes from your models.
運用 TensorFlow 生態系統建構及微調模型
Explore an entire ecosystem built on the Core framework that streamlines model construction, training, and export. TensorFlow supports distributed training, immediate model iteration and easy debugging with Keras, and much more. Tools like Model Analysis and TensorBoard help you track development and improvement through your model’s lifecycle.
To help you get started, find collections of pre-trained models at TensorFlow Hub from Google and the community, or implementations of state-of-the art research models in the Model Garden. These libraries of high level components allow you to take powerful models, and fine-tune them on new data or customize them to perform new tasks.
TensorFlow provides robust capabilities to deploy your models on any environment - servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, FPGAs. TensorFlow Serving can run ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).
If you need to analyze data close to its source to reduce latency and improve data privacy, the TensorFlow Lite framework lets you run models on mobile devices, edge computing devices, and even microcontrollers, and the TensorFlow.js framework lets you run machine learning with just a web browser.
在 Colab 中試用使用 TensorFlow Serving 提供模型
The TensorFlow platform helps you implement best practices for data automation, model tracking, performance monitoring, and model retraining.
Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success. TFX provides software frameworks and tooling for full MLOps deployments, detecting issues as your data and models evolve over time.
如對機器學習的原則和核心概念有基本瞭解，使用 TensorFlow 會更加得心應手。瞭解並應用機器學習的基礎實務，進而培養技能。