We performed a comparison between DataRobot 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."DataRobot can be easy to use."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"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."
"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."
"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."
"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."
"It's got quite a big community, which is useful."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
"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 cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"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."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"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 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."
"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."
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DataRobot is ranked 13th in AI Development Platforms while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. DataRobot is rated 8.0, while TensorFlow is rated 9.0. The top reviewer of DataRobot writes "Easy to use, priced well, and can be customized". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". DataRobot is most compared with Amazon SageMaker, RapidMiner, Microsoft Azure Machine Learning Studio, Datadog and Alteryx, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Hugging Face. See our DataRobot vs. TensorFlow report.
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