We performed a comparison between OpenVINO 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."The initial setup is quite simple."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"The features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"At this point, the product could probably just use a greater integration with more machine learning model tools."
"The model optimization is a little bit slow — it could be improved."
"It has some disadvantages because when you're working with very complex models, neural networks if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model. It is difficult."
"The solution is hard to integrate with the GPUs."
"TensorFlow Lite only outputs to C."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."
"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
"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."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
Earn 20 points
OpenVINO is ranked 11th in AI Development Platforms while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. OpenVINO is rated 8.6, while TensorFlow is rated 9.0. The top reviewer of OpenVINO writes "A free toolkit providing improved neural network performance". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". OpenVINO is most compared with PyTorch, Azure OpenAI, Google Cloud AI Platform, Google Vertex AI and Microsoft Azure Machine Learning Studio, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, Hugging Face, Azure OpenAI and IBM Watson Machine Learning. See our OpenVINO vs. TensorFlow report.
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