Yes, TensorFlow.js can be used for both training and inference. TensorFlow.js is a machine learning library that allows you to build, train, and deploy machine learning models directly in the browser or on Node.js. It provides a flexible API for both training and inference tasks.

  For training, TensorFlow.js provides a high-level API called tfjs-automl that allows you to train machine learning models directly in the browser. It supports various algorithms such as linear regression, logistic regression, and neural networks. You can use this API to define and train your models using JavaScript and the available datasets.

  In addition to the tfjs-automl API, TensorFlow.js provides lower-level APIs for more advanced training scenarios. The core API, tfjs-core, allows you to define and train complex neural network models using layers, optimizers, and loss functions. You can also use tfjs-node or tfjs-node-gpu to train your models on Node.js using the available hardware acceleration provided by GPUs.

  Once you have trained your model, TensorFlow.js also provides APIs for inference. You can use the trained model to make predictions on new data, whether it's in the browser or on Node.js. The tfjs-converter API allows you to convert models trained in Python with TensorFlow to a format that can be used in TensorFlow.js. This enables you to take advantage of pre-trained models and use them for inference tasks.

  Overall, TensorFlow.js is a powerful tool that allows you to perform both training and inference tasks for machine learning models directly in the browser or on Node.js. Whether you're building a web application or a server-side application, TensorFlow.js provides the necessary APIs to efficiently train and deploy machine learning models.