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'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.

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 am using TensorFlow for many computing projects, such as image data analysis, data compressions, and data processing.

I have used TensorFlow GS, TensorFlow for mobile, and TensorFlow Lite. The way things operate is changing from a server-side to edge computing and mobile devices. I have been working on how to reduce the resource usage, we have downgraded and should have good performance with the best accuracy in our machine and models.

Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers.

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.

There should be better integration and standardization with different operating systems. We need to always convert from one model to another and there is not a single standardized model output that we could use on different platforms, such as Intel x56, x64 based, AR-based, or Apple M1 chips.

I have been using this solution within the last 12 months.

TensorFlow is a reliable solution, but we have not explored all the aspects of TensorFlow. We have been building our customized applications, such as libraries, features, or functions. We only use the features that allow our application to work. Different areas need to be researched on a low level to make them more efficient.

We have been working on specific applications and any model built on TensorFlow can be applied to any scalable level. The solution has built-in scalability.

I have not needed to use technical support.

I did not require a license for this solution. It a free open-source solution.

I rate TensorFlow a ten out of ten.

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.