TensorFlow Room for Improvement
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.View full review »
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.View full review »
I don't have too much experience with the dashboards in the solution, however, it's possible they could be improved.
I need to have more experience in the security aspect of the solution. It could, however, always develop this area more.
It would be nice if the solution was in Hungarian. I would like more Hungarian NLP models.View full review »
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.View full review »
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.
Data Scientist at a university with 5,001-10,000 employees
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.View full review »
Project Manager at INFOCOM Ltd
There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves.View full review »
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.View full review »
Chief Technology Officer at a tech services company with 51-200 employees
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.View full review »