We performed a comparison between IBM Watson Machine Learning 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."Scalability-wise, I rate the solution ten out of ten."
"It has improved self-service and customer satisfaction."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive."
"It is has a lot of good features and we find the image classification very useful."
"The most valuable aspect of the solution's the cost and human labor savings."
"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."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"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."
"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."
"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."
"TensorFlow provides Insights into both data and machine learning strategies."
"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."
"In future releases, I would like to see a more flexible environment."
"The supporting language is limited."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use."
"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."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"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."
"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."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"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."
"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."
IBM Watson Machine Learning is ranked 9th in AI Development Platforms with 6 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. IBM Watson Machine Learning is rated 8.0, while TensorFlow is rated 9.0. The top reviewer of IBM Watson Machine Learning writes "A highly efficient solution that delivers the desired results to its users". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". IBM Watson Machine Learning is most compared with Google Cloud AI Platform and Azure OpenAI, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, Hugging Face and Google Cloud AI Platform. See our IBM Watson Machine Learning vs. TensorFlow report.
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