We performed a comparison between Hugging Face and TensorFlow based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."My preferred aspects are natural language processing and question-answering."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"TensorFlow provides Insights into both data and machine learning strategies."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."
"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."
"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."
"The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily."
"Implementing a cloud system to showcase historical data would be beneficial."
"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"TensorFlow Lite only outputs to C."
"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"Personally, I find it to be a bit too much AI-oriented."
"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
"The solution is hard to integrate with the GPUs."
Hugging Face is ranked 7th in AI Development Platforms with 3 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Hugging Face is rated 9.0, while TensorFlow is rated 9.0. The top reviewer of Hugging Face writes "A comprehensive natural language processing ecosystem offering a diverse range of pre-trained models and a collaborative platform". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Hugging Face is most compared with Google Vertex AI, Azure OpenAI, Replicate, Google Cloud AI Platform and Amazon SageMaker, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Azure OpenAI. See our Hugging Face vs. TensorFlow report.
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