We performed a comparison between Google Cloud AI Platform 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."A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"The initial setup is very straightforward."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"It's got quite a big community, which is useful."
"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."
"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."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"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."
"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 solution can be improved by simplifying the process to make your own models."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"Customizations are very difficult, and they take time."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"The initial setup was straightforward for me but could be difficult for others."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"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 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."
"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."
"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."
"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."
"TensorFlow Lite only outputs to C."
"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."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
Google Cloud AI Platform is ranked 6th in AI Development Platforms with 7 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Google Cloud AI Platform is rated 7.8, while TensorFlow is rated 9.0. The top reviewer of Google Cloud AI Platform writes "An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Google Cloud AI Platform is most compared with Microsoft Azure Machine Learning Studio, IBM Watson Machine Learning, Google Vertex AI, Azure OpenAI and PyTorch, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Wit.ai. See our Google Cloud AI Platform vs. TensorFlow report.
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