TensorFlow Reviews

I use the solution to create AI models for our object recognition projects.

The platform has significantly impacted model development, particularly its application in YOLO models like YOLO v8.

The process of creating models could be more user-friendly. 

I've been working with TensorFlow for about a year or two.

The product has good stability. 

The solution is mainly used to train efficient models, similar to what Tesla might use to recognize its surroundings. Once a model is created, it's unnecessary to use it daily.

I rate the scalability around seven or eight. It largely depends on the GPU you use and the quality of the annotations provided.

I have yet to use technical support, as the solution is open-source. The available documentation is extensive and helpful.

Positive

The initial setup is straightforward and manageable.

Currently, the platform is deployed on-premises in our organization. However, it can also be used in Google Colab, though using it intensively may incur additional costs, particularly for GPU resources.

Deploying it for training models took about a week, especially if you need to familiarize yourself. The process involves scripting and annotating models, which are crucial steps.

The platform integrates well with other tools, especially Python, which we use to create models. These models can be deployed on mobile devices, which perfectly suits our requirements.

It supports our AI-driven initiatives very well by producing AI models, which is its primary function.

I recommend it for those seeking specialized scripting. However, it's important to consider other options as well. It is better suited for specialists in the field and is less user-friendly than general tools like Excel.

I rate it overall at six out of ten. While it is a powerful tool, other software options are slightly simpler for training models.

I use TensorFlow for R&D, and at a higher level, I do machine learning for prescriptive maintenance.

TensorFlow provides Insights into both data and machine learning strategies. The R&D with TensorFlow gives me that.

There's a product called DMWay, and I worked with some data scientists who used it. It could output models to several different software languages. And TensorFlow, or TensorFlow Lite at least, outputs to C. For instance, adding packages to output to C# or JavaScript would be very useful.

I've used the solution for five or six years.

I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist.

In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation.

Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant.

I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.

We have factorization software in our product, and we use natural language processing. We have tested TensorFlow on NVIDIA chips for ten years and even had TensorFlow running on IBM Power chips before IBM and Google did. We use the open-source framework, and our primary use case for TensorFlow is pixel analysis of images, and image analytics.

We considered TensorFlow because we had a particular strategy for the hardware. We believe in a combination of central computing and edge computing, and what made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device, which means you can build an AI continuum using massive power in data centers on that cluster while having certain jobs performed at the edge. You can literally have the same code base work in the cluster and at the edge, providing a huge advantage. TensorFlow aims to create a homogeneous landscape all the way from the camera in the field, which has a processor behind it so that you can do all kinds of image processing in each of the cameras. And if you don't have 50,000 cameras, you can bring preprocessed data to the center because taking the raw data from the camera is impossible. It would also be impossible if you had 50,000 cameras.

It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers.

Independent of the hardware platforms or cloud platforms, in this case, we would like to run TensorFlow across different hyperscalers. The hyperscaler platforms are Google, Amazon, and Microsoft, and yes, you can run TensorFlow on all three of them, but you need to make tweaks. From the perspective of an independent software supply and software development company, it would be so much easier if the solution would be pluggable across any infrastructure.

We know that that's a tall order because the solution links to the different setups the hyperscalers have. Hardware is not completely and equally set up. If TensorFlow could provide some layer that would make that easy, that would benefit us.

Back in 2013-14, though it's now no longer a challenge nor a commercial issue, we were working with the hardware division of IBM. The combination of IBM processors and NVIDIA processors reacted differently to the processor combination of Intel and NVIDIA, which was on the cloud. This meant that we, as an ISV, had to get the solution to work on different hardware.

I've used TensorFlow for around ten years since my organization became an NVIDIA partner.

I rate TensorFlow an eight out of ten for its stability.

TensorFlow is scalable, and I rate it a nine out of ten on scalability. We have five people working on TensorFlow. They are development and research engineers.

When we needed it, we received excellent technical support.

Positive

We found the initial setup to be easy, but I'm not sure if that depends on one's technical staff. We are extremely lucky to have many highly versed people, so we find it easy.

We had people proficient in AI in 2013. We have people with previous experience in AI and high-performance computing. That's also why we could work with the IBM Power chip and get Tensor to run on it. We did that work ourselves.

The solution has been deployed single-handedly.

I rate TensorFlow's pricing a five out of ten.

We're also using Azure OpenAI. Not the SaaS OpenAI open to the public, but the version Microsoft offers to enterprises.

If, like us, you're interested in one AI framework that runs both on edge and central computing, then definitely look into TensorFlow. In other words, if you need AI to extend right into the edge devices, TensorFlow is super.

I rate TensorFlow an overall nine out of ten.

I have mainly used the solution for solving deep learning problems like image classification and the NLP.

It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it. 

The solution is hard to integrate with the GPUs. 

It is a stable solution. 

TensorFlow is scalable. I rate the scalability a nine out of ten.

The initial setup is easy. The deployment depends on the nature of the problem and its classification by the organization. It is also dependent on the internet availability.

It is a recommended solution but at the beginner level, they should have some assistance. I rate the overall solution a ten out of ten. 

TensorFlow can be deployed in the cloud or on-premise.

TensorFlow can be used for a lot of things, such as prediction and identification.

TensorFlow has improved a lot in my company because it can do useful predictions. If you can predict, you can optimize, and you can make your business from reactive to proactive. That's what's the business value in TensorFlow.

The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market.

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.

There is a learning curve to using this solution well.

I have been using TensorFlow for approximately five years.

TensorFlow is highly stable and that is why people use it.

The scalability of TensorFlow is good.

There are approximately three people using this solution in my company. There are not that many people using it because it is rather complex.

I have not used the technical support from TensorFlow.

The initial setup of TensorFlow is complex. You need to use the GPU and it has to be done properly.

I am using the open-source version of TensorFlow and it is free.

Microsoft and IBM have tried to make a similar solution but TensorFlow is the best in the market.

I would recommend this solution to others.  

There are a lot of companies, that create new packages around TensorFlow, they make it easier to use, but whenever you make it easier to use, you create some limitations.

I rate TensorFlow a nine out of ten.

I use the solution for NLP tasks that involve building neural networks. 

TensorFlow is an efficient product for building neural networks. 

Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high.

I have been using the product for three years. 

The product is stable. 

I haven't contacted the solution's support team yet. 

The solution's deployment is easy. 

The solution is free. 

Using TensorFlow is beneficial, especially for building models that are hard to create with other tools. It is easy to learn and great for NLP and computer vision tasks, which often require complex neural networks. TensorFlow has strong community support. I recommend using TensorFlow because you can build models easily and achieve good accuracy. Additionally, TensorFlow has JavaScript support, which allows you to run models on web servers and web pages.

I rate it a nine out of ten. 

I use the solution for computer vision and object recognition.

I can find websites with a lot of open-source codes and this is the use case for me as well.

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. 

I have been using TensorFlow for about 12 months and am using the latest version of the solution.

I have not encountered any stability or performance issues with the solution until now.

I think the solution is quite scalable. I am using the tool on my own CPU and I do things quickly using the solution. I am considering increasing the usage of the tool in the future.

The setup is relatively straightforward. It is not easy nor overly complicated, but doable. It takes time to create your connectors. You will have to spend time thinking about how you are going to do things.

There is ROI with the use of the solution. You have the right application with the appropriate implementation and business case which can really make a lot of impact.

I am using the open-source version of the tool. It is free of cost and does not have any hidden costs either.

I have read some comparisons about Caffe, OpenAI, and TensorFlow. I found TensorFlow to be most adaptive to my needs. Caffe helps you build everything fast. However, it is not very dedicated to some applications. TensorFlow is more generic and helps to analyze various applications.

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.

My advice to anyone willing to try the solution is that you need to do a lot of research on GitHub, AI websites, or other websites where people exchange their knowledge.

I've used TensorFlow for image classification tasks, object detection tasks, and OCR.

TensorFlow is easy to implement and offers inbuilt functions for various tasks. It supports image classification, object detection, and OCR. The availability of an app makes it simple to integrate OCR functionality directly to mobile applications.

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications.

I have worked with TensorFlow for two years.

I initially used TensorFlow and later switched to PyTorch since it provides more control over functionalities, allowing for the creation of custom functions.

I am not familiar with the pricing setup cost and licensing.

I rate TensorFlow seven out of ten. The availability of inbuilt functions makes it suitable for easy development, but PyTorch provides more control.

We have a project that a Canada-based client is expecting us to develop. If there is a hardware product, it's a mirror LCD device, that is installed in your home and when you start doing an exercise, our AI algorithm will detect what kind of exercise, whether you're doing pushups, jump, etc. We also detect what kind of hardware equipment is being used. We also use TensorFlow to count. 

TensorFlow improves my organization because our clients get a lot of investment from their investors and we progressively improve the products. Every six months we release new features. 

They have a very good vision and roadmap for the next two years. 

TensorFlow is like a library. PyTorch is also a library. These are deep learning libraries that provide a set of functions. Ultimately you have to build a framework. TensorFlow as a whole is useful to us because we use a lot of functions, like activation functions or volition functions, feature mapping, and feature extraction. 

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. When there are more and more objects involved with the model, the models get confused. So maintaining the accuracy and speed with the number of classes is the biggest area for improvement. It is a major challenge that we are seeing right now and we are trying to solve the problem.

It's quite stable. It also helps while we have to take it to the browser platform as well as the Android platform because Google is developing in such a way that we can easily migrate. I'll definitely go with TensorFlow when we have to deal with the different platforms. We can easily convert into TF Lite. We can run the model into the browser, as well as on the Android platform.

We have 13 developers and 10 developers mainly focus on TensorFlow, PyTorch, and all the deep learning things. The rest of the developers are C+ and computer vision developers.

If any maintenance is required our team is capable.

Scalability is a major challenge because under the exercise project right now, we have integrated 14 exercises, one port and client is targeting more than 50 exercises. That's where we need scalability. 

Sometimes when we face some issues, we mostly get solutions from stakeholders. So we are not using that much technical support from the TensorFlow team.

TensorFlow is from Google, PyTorch is from Facebook. PyTorch is mostly compatible with Python. If you have to consider it in different platforms like different languages C+, Android, then TensorFlow is really good. In that sense, I'd go with TensorFlow, but PyTorch is really stable. It's only helpful when we are dealing with the Python language. That's where it's really helpful. So both have some advantages and disadvantages.

We got some help from the internet blogs but by now our team is really capable. If there are any issues or errors with a particular version, they can immediately deploy changes. It's now a completely smooth process.

We use the open-source version.

There are always new versions coming out and some versions have issues while some versions don't. When you deploy with the latest version, just make sure that all the systems work as expected when you're deploying. 

I would rate TensorFlow an eight out of ten. 

The main purpose of TensorFlow is to  develop neural networks for data science projects. For example, I had a project about a super-resolution GAN, which is a model that you give a low-resolution image, and it will complete the details for you. I used Keras and TensorFlow for this model and it was really easy to use. The time to implement was simply minimal in comparison to the time for testing, logic, and high-level implementation. That was the highlight of my academic project. For a client, I used TensorFlow and Keras to develop a predictive heat map for orders. He wanted to build a predictive model for a taxi company. They wanted to tell the drivers, "Okay, this area has more probability of having higher orders than another area." I used TensorFlow and Keras to develop a model to predict the areas which have a higher probability and built a heat map to show the drivers. That is actually the highlight of my industrial project. It was a client on Upwork.

For me, it's the simplicity of using the built-in layers. The load from generator method saves us a lot of time and memory in terms of development and the learning process of models, especially networks that have huge parameters. They will need a hundred terabytes of runs but with this method, which is amazing. All the methods of TensorFlow are consistent with each other. So it will save the developer a lot of time and actually, that reflects on the client as well. So the client would get a  high-quality product with a minimal budget. This enables clients with limited budgets to actually hire developers that can develop high-quality models using TensorFlow.

The most valuable feature for TensorFlow is the ability to use CoLab. It's actually also using Torch, but in TensorFlow according to my experience, it's much, much easier to do than the integration with Google CoLab. It's pretty simple to use Google CoLab Pro and use TensorFlow models. It's not a feature, but the best thing about TensorFlow and Keras is that it is the most common in the world and they have huge communities. Whatever error you have, you can actually Google that error and you can get it done in five minutes. So that is, I think, really unique about TensorFlow. I never actually thought about developing a system like TensorFlow. It's so huge and it needs a lot of developers to maintain, but if I want to develop a sub-system that actually helps me to solve a task, I can do that in just two days to develop benchmark models in TensorFlow. If I had to develop this from scratch I would probably need 20 days to a month to develop it myself from scratch. 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, improved the lives of many people around the globe. If it was a licensed product, a lot of people, for example, in the Middle East or the third world countries, would not be able to help their own communities because of a substantial license fee they cannot afford. The biggest lesson I learned is to have an open-source platform that could impact the world and make it a better place. You get that with TensorFlow.

If I want to develop my own gradient descent, and I want to use the TensorFlow grading descent, but implement it in my own way, it can be difficult. However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and 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. But this feature, allowing you to write bespoke code to an implementation of TensorFlow would be really great. Another thing I think that TensorFlow would be much more optimized is to have better CPU versions. I know the problem with Python in general, it lets you only use one thread in the CPU. But even while using TensorFlow, it uses two threads. For example, if I have a high powered CPU, I cannot use it. For example with my laptop, I have a high-powered CPU and I'm using Ubuntu, but my GPU is not recognized. So I can use the CPU, but it's not really optimized for this purpose. Huge calculations require GPU's. I think that could be the second thing that could be optimized. I think TensorFlow 2 has huge improvements over TensorFlow 1. However, it would be really nice if we can actually somehow use the code written in TensorFlow 1, to incorporate it into TensorFlow 2. It generates a lot of errors and you have to change a lot of code and settings. What we can optimize is to actually have consistency between the versions. So TensorFlow 2 is actually a different product, to TensorFlow 1.

I have been using TensorFlow for around one year and a half. I use it for projects. So it has been three years since I took a TensorFlow tutorial or course about TensorFlow. I started applying it in industrial projects and academic projects for one year and a half.

For TensorFlow 2, I think it's very stable. For that version. Stability is inconsistent between the versions of TensorFlow 1 and TensorFlow 2. I think from the TensorFlow functionality side, you don't really need any maintenance, but from the developer's side, you do need maintenance. In my experience, I deployed three or four projects and they just worked consistently. I never had any problems except the problem that I had on my own, bespoke code, but not the code that was actually provided in the TensorFlow library.

I have never used it on multiple servers, as usually I have only dedicated servers, and tend to deploy on on those. I know that it could be scalable to multiple servers like AWS Lambda. I'm not sure, but it can be, although I have never worked with it.

I once posted a question in the questions section on the TensorFlow website and I got an answer in two days and it was really helpful. That actually helped me solve the problem that I had. Apart from that, I didn't really use it because generally, I can find my answer by searching for the error on Google search. It has very good documentation and community support.

I only worked with PyTorch and TensorFlow. I used PyTorch two and a half years ago. I started working with PyTorch and it was difficult for a young person who simply just wants to run models quickly. So in PyTorch, one has to have a huge experience until they deployed their first model. This is really demotivating for someone who is starting to learn the principles. That's a negative for PyTorch and it's a plus for TensorFlow. There is this simplistic structure of TensorFlow such that you can write a fully connected layer model in three lines, and run it on another data set in three or four lines. That is really nice in comparison to PyTorch as you have to write three different classes and data loaders. It's more time consuming to run in PyTorch. So I think it's another positivity for TensorFlow. I can't think of anything you can achieve with PyTorch that you cannot with TensorFlow. That's why I actually just stopped using PyTorch after awhile.

It is very easy to set up as technically it only uses one or two lines if you are using Conda. It is easy to build a simple neural network, by just following the tutorial three lines and that's it. We have running neural networks. Pretty easy. You can host it on your own computer. It's a Python pip package. You can host it on your computer, on a server. The client that I worked with for the heat map prediction, was hosted on a server. It's a website. So the server for calculations was hosted on Amazon web services. So it could be all of these.

I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free. 

I had a problem with it during one implementation. I assumed that the data would be small. I think before implementing your TensorFlow model, it's crucial to note what is the size of your data and will it increase in the future? Usually, a developer wants to develop the model as easily as can it be. So they just tend to load all the data in memory and then run it into a flow model. So that is really problematic if your data is huge. That's why it's best for the developer before they write any line of code to check the data. If it doesn't fit in memory, they can use the TensorFlow functionality of load from a generator and this way they can actually have just one image in the memory at a time per thread. So it's really amazing. So I think that that is the tweak that I would advise developers to have, before developing their model.

I would rate TensorFlow 9 out of 10.