TensorFlow 2025 Verified Reviews, Pros & Cons

The big advantage of TensorFlow is also the serving, with TensorFlow serving it is quite easy to deploy the model (literally a matters of minutes with reasonable performance), however performance wise it is not always the best, I often get better throughput with ONNX conversion of the model then deployment with TensorRT at then expense of more intermediary steps (tradeoff depending on the load expected for the model). I think TensorFlow got a bad wrap in the community due to the handling of the transition from version 1 to version 2 that was a bit chaotic, similarly when Google dropt the support of TensorFlow-Swift fears of "yet another project that Google will kill" intensified, but TensorFlow 2 can still be a good choice for a lot of models especially BERT based (NER, QA, etc.) ### Pros - Model serving - Keras ... - Lot of open source projects based on it (RL/GNN/etc.) - Lot of pre-finetuned BERT based models ### Cons - Too much abstraction - Conversion of PyTorch models not that obvious sometimes ### Likelihood to Recommend Well suited: - pretrained BERT-base model ready to deploy - IoT with TensorFlow lite and the edge TPUs - Domain where datasets are available in Huggingface (e.g., medical model) Less well suited: - Small project due to the complexity/less resource to learn - New model tends to use PyTorchPTVetted Review