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."It has improved self-service and customer satisfaction."
"The most valuable aspect of the solution's the cost and human labor savings."
"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."
"Scalability-wise, I rate the solution ten out of ten."
"It is has a lot of good features and we find the image classification very useful."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"TensorFlow provides Insights into both data and machine learning strategies."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"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."
"In future releases, I would like to see a more flexible environment."
"The supporting language is limited."
"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."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
"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."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"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."
"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."
"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."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
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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