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 features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"The initial setup is quite simple."
"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."
"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."
"TensorFlow provides Insights into both data and machine learning strategies."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"It's got quite a big community, which is useful."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"At this point, the product could probably just use a greater integration with more machine learning model tools."
"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."
"Personally, I find it to be a bit too much AI-oriented."
"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 nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"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."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"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."
"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."
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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