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."The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"It is has a lot of good features and we find the image classification very useful."
"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 has improved self-service and customer satisfaction."
"The most valuable aspect of the solution's the cost and human labor savings."
"Scalability-wise, I rate the solution ten out of ten."
"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."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"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."
"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."
"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."
"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."
"In future releases, I would like to see a more flexible environment."
"The supporting language is limited."
"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."
"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."
"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."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"TensorFlow Lite only outputs to C."
"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."
"The solution is hard to integrate with the GPUs."
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 Azure OpenAI. See our IBM Watson Machine Learning vs. TensorFlow report.
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