TensorFlow Reviews

I use this solution out of personal interest — for AI.

Our company doesn't use it too much. We tend to use OpenML, which is included in the OpenCV package, OpenML. 

It's got quite a big community, which is useful.

I tend to find it to be a bit too much orientated to AI itself for other use cases, which is fine — that's what it's designed for. Personally, I find it to be a bit too much AI-oriented.

We have been using TensorFlow for roughly five years.

We haven't had any issues stability-wise or scalability-wise. I tend to use it with Python. It seems okay. It works fine. 

The initial setup was straightforward.

I would happily recommend TensorFlow to people who are looking to use it for AI. Overall, on a scale from one to ten, I would give this solution a rating of eight.

I primarily used the solution for computer vision applications, for example, detection and segmentation, and OCR.

We used an architecture from a published paper. It was based on TensorFlow and we upgraded it and developed on it. I also worked on face verification and likeness detection. We are working on anti-spoofing detection. We did some things around face verification and likeness detection. I used TensorFlow specifically.

I've also used the solution to detect hands, tracking customers in the supermarkets, and using the solution for detecting the pickup and dropping of objects from shelf to basket in the supermarket.

Lately, I've been working on a project in Arabic that is designed to detect handwriting. I also use PyTorch to help with this task.

For one particular project, we did an extraction of the Arabic language from a crucial document, like an ID. We needed to capture the ID using the application so that the application sends the ID to the server. We needed to make an Egyptian ID detection on mobile. I built a simple commercial network to customize the ID and converted it into TensorFlow White and made some compositions to make it faster to run. We deployed it on the mobile. For this bot, there's full support in this area, which is great.

The solution is quite useful for production. It tends to provide for digital devices or mobile devices. You can deploy your model on Android or iOS. I did that before on Android. It provides TensorFlow GS or JavaScript to run TensorFlow applications in the browser. 

It's quite a valuable solution when we go to production.

Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful. 

Overall, the solution has been quite helpful. I can't recall missing any features when I was using it.

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.

The solution is quite stable. It's reliable. It doesn't crash or freeze. There are no bugs or glitches to deal with. We've been happy with the performance.

The scalability of the solution is good. When it comes to TensorFlow (or PyTorch) you can train on multiple GPUs at the same time, and multiple machines at the same time, also. 

In TensorFlow, I didn't train on multiple GPUs, yet, however, I know it's very easy and straightforward. I've been through it. Scaling should be a problem for a company. If they want to scale, they can.

I've never been in touch with technical support. I can't speak to their level of knowledge or how quickly they respond.

I also use PyTorch and Amazon SageMaker.

Amazon Solution provides the how-to where we can use PyTorch and TensorFlow to train models on huge datasets on AWS end-user. They are complementing each other on a project.

The entire implementation process is quite straightforward. It's not complex at all.

The deployment is very fast. You can have it up and running in five minutes. You just go and install the version you would like to use and you are done. It's all very simple.

I always go with the deployment with TensorFlow. I don't try to use PyTorch in this area. A year ago, when I was deploying a semantic segmentation model on the server, each time I sent a request that I need to reload the model input that ends.

It's my understanding that the version we use is free. It doesn't cost any money.

When we did the utilization applications, we were deploying on digital ocean servers. For the projects that I'm working on now, we are planning to deploy it on its own port attached to the robot. We haven't done it, yet. We are finishing the project right now. For deploying the solutions, I deploy them on the digital ocean. 

I'd recommend the solution. I'd also recommend users considering the solution do a bit of studying. There are some great courses on Coursera and there's a recent one called DeepLearning.AI that is extremely useful.

Overall, as I use the product pretty much everywhere, I would rate it at a ten out of ten.

I use this solution to create Neural Networks, which are computer algorithms for the recognition of objects. This is done based on the SL object that predefines it.

Most of our experience is computer related, but in most cases, we work with images.

Our company was working on a specific project that required a graph. It is now under development and TensorFlow allows us to implement this functionality for the customer who needs to work with recognizing and defining the special mark on the student's workbooks.

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.

There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves.

I have been using TensorFlow for one year.

I have experience not just in TensorFlow, but in the TensorFlow Keras, beginning from TensorFlow 2.0, there are package Keras in TensorFlow. Using this cache, I have created some Neural Networks on Python.

I am using the latest version.

It's a stable product.

It's a scalable solution and we can scale it for different tasks.

We have two specialists that are connected to TensorFlow.

We have not contacted technical support.

Before using TensorFlow, we used different neural networks that were based on Darknet.

The initial setup was easy. There is a lot of documentation available and it was not a problem for us.

It was easy to install.

Setting up TensorFlow on the local computer will take one to two hours to complete. However, if it is for an industrial product that has entered the market and needs to work in the real environment, it would depend on the goal and task that we are working on. 

We completed the installation ourselves without any external help.

Maintenance is based on the customer's needs. We have approximately 40 developers, so if the customer requires maintenance and support then we can provide that for them.

We are using the free version.

I would recommend TensorFlow for techniques that need to develop Neural Networks. I would also recommend PyTorch.

I would rate this solution a nine out of ten.