TensorFlow
Definição
TensorFlow é um framework de aprendizado profundo com foco em implantação em produção. Keras é a API de alto nível. Ele suporta CPU, GPU, TPU, and mobile/edge (TFLite). É an alternative to PyTorch; TensorFlow and Keras are strong for production pipelines, infrastructure (TF Serving, TPU), and mobile (TFLite). Used for vision, NLP, and recommendation systems; the ecosystem includes TensorFlow Hub for pretrained models.
Como funciona
Construa modelos com Keras (API sequencial ou funcional) ou a API TensorFlow de nível inferior. Treine com execução eagertion (default, PyTorch-like) or graph mode (faster, exportable). Export to SavedModel (standard serving format) or TFLite (mobile/edge, with quantization support). TensorFlow Hub and Keras Applications provide pretrained models for transfer learning. Distributed training uses tf.distribute; deployment uses TF Serving, Vertex AI, or on-device TFLite. The pipeline from data (tf.data) to training to serving is well integrated.
Casos de uso
TensorFlow suits production pipelines, mobile/edge (TFLite), and quick prototyping with Keras and Hub models.
- Production ML pipelines and serving (por ex. SavedModel, TF Serving)
- Mobile and edge deployment (TFLite)
- Quick prototyping with Keras and pretrained models from Hub