"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 initial setup is quite simple."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"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."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"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."
"The model optimization is a little bit slow — it could be improved."
"At this point, the product could probably just use a greater integration with more machine learning model tools."
"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."
"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."
"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."
"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."
"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."
"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 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."
OpenVINO toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel hardware, maximizing performance. The OpenVINO toolkit includes the Deep Learning Deployment Toolkit (DLDT).
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
OpenVINO is ranked 3rd in AI Development Platforms with 3 reviews while TensorFlow is ranked 2nd in AI Development Platforms with 10 reviews. OpenVINO is rated 8.6, while TensorFlow is rated 9.2. The top reviewer of OpenVINO writes "Open-source, easy to integrate, and perfectly tailored to the Movidius chipset". On the other hand, the top reviewer of TensorFlow writes "The generator saves us a lot of time and memory in terms of development and the learning process of models". OpenVINO is most compared with PyTorch, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform and Caffe, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, IBM Watson Machine Learning, Wit.ai, Infosys Nia and Caffe. See our OpenVINO vs. TensorFlow report.
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