We performed a comparison between Azure OpenAI 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."Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."
"The product's initial setup phase was pretty easy."
"Generative AI or GenAI seems to be the best part of the solution."
"The high precision of information extraction is the most valuable feature."
"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."
"We have many use cases for the solution, such as digitalizing records, a chatbot looking at records, and being able to use generative AI on them."
"Azure OpenAI is very easy to use instead of AWS services."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"TensorFlow provides Insights into both data and machine learning strategies."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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 most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"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."
"Azure OpenAI is not available in all regions, and its technical support should be improved."
"The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."
"We encountered challenges related to question understanding."
"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."
"Deployment was slightly complex for me to understand."
"There are no available updates of information that are currently provided."
"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."
"There is room for improvement in their support services."
"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 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."
"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"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."
"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."
Azure OpenAI is ranked 2nd in AI Development Platforms with 17 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Azure OpenAI is rated 8.0, while TensorFlow is rated 9.0. The top reviewer of Azure OpenAI writes "Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Azure OpenAI is most compared with Google Vertex AI, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Hugging Face and OpenVINO, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Infosys Nia. See our Azure OpenAI vs. TensorFlow report.
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