TensorFlow Reviews

I worked for a French company. They used TensorFlow for image classification. after that, I started working with a Russian-American Company who used TensorFlow mainly for object detection. TensorFlow is very good at object detection. We also used it once for natural language processing and audio processing, but I was not directly involved in that project. I was just assisting with deployment issues. We have some clients which wanted us to deploy on the cloud. Alternatively, some clients are releasing Tenserflow on some new edge devices, as an alternative to deploying on the cloud. It is going to be called NextGen AI or something like that from AWS. We use it for all aspects. including data processing, training, and sometimes deployment, but sometimes the use cases differ in practice for ML. As a result, we sometimes stay with TensorFlow or move into AWS specific architectures.

TensorFlow has benefitted us by enabling faster training and deployment. With TensorFlow, we don't really need any more DevOps to do the deployments. Even data scientists can do the deployment part. This has saved about 30% of the time we used to take for deployments.

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. Especially the part where you could train the model again, then evaluate it if it's better than the previous versions. It will do the deployment on its own. The end-users will not really see the change, as the update takes place without any downtime.

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.

We have been using Tensorflow since 2017, so three years.

It's very stable. So we usually don't get any problems. Once any bugs are fixed, you shouldn't have any problems with TensorFlow. Once the deployment process is completed, you can monitor your model and datasets. You can monitor your model to ensure it is correctly deployed and it's working as it's supposed to do, including services.

TensorFlow is very scalable.

I've never really contacted TensorFlow support, but definitely, I can say you don't really need to do that because the support, like the community is pretty strong. Whatever problem you face, there's always going to be some Stack Overflow answer for it or at least some GitHub issue where you can find your solution.

We used PyTorch and MXNet. A couple of my friends actually used MXNet, but I did not use it personally. Now I work mainly between PyTorch and TenserFlow.

Setup got easier with TensorFlow 2. With TensorFlow 1 it was a bit more complicated. There are fewer compatibility issues with the newer version. Training takes at least a week. Deployment usually takes one or two days. With regards to deployment, this changes depending on the client. The usual method is to get our TensorFlow models up and running and then we have to convert them into specific formats depending on the client's requirements. Some clients actually require AWS specific formats. To incorporate that we usually just convert our TensorFlow models to AWS compatible models.

We use in-house teams. I think the ML team has around 20 people. There is a team in Russia, Ukraine and the U.S. I am part of the team in Russia. In Russia, we have around 30 people who have used TensorFlow, including data analysts. They basically handle data pre-processing. We also have ML engineers and ML Ops.

TensorFlow is free, so cost is not an issue.

PyTorch is very flexible, and definitely more flexible than TensorFlow. However, TensorFlow allowed us to deploy our models faster and more robustly. With TensorFlow, you don't encounter a lot of errors. PyTorch is faster to prototype with. So it's very flexible. You can do all the changes that you want, but it's not very stable, whereas TensorFlow is more of a stable solution but is not very flexible. We looked at other alternatives, but they don't scale to large problems.

Have a look at TensorFlow extended. It's very useful. Especially if you know how to use the old system. It will speed up the process of deploying your model. Don't reinvent the wheel. There's always going to be a good GitHub repo out there which kind of answers your solution. You shouldn't really spend a lot of time trying to build the new models where there is some other open source project that actually did a good job of the modelling part. You definitely need to have your own pipelines for this process. Try to build the pipelines that automate most of the tasks for you. Then all you need to concern yourself with is just the architecture. Obtain a pipeline template from GitHub of what you are trying to achieve, amend it for your needs and then you are ready to go. Your model is training already. I would rate TensorFlow 8 out of 10.

I have experience in NRP and time series forecasting and also in marketing-relevant tasks.

For example, I've used the workaround cutoffs to create a deep learning network to classify binary classification. I've done binary classification tasks and multi-label classification tasks. The multi-class classification is based on Hungarian and English texts. I have an ongoing project, where I created an LSTM and this LSTM is able to classify the text for cryptocurrencies. 

Pre-trained models are very important in terms of flow. It's a great opportunity to create very fast, new, deep learning models. 

Before version 2.X, PyTorch had features that were better than this product. Now that it's been updated, it's got all of those missing features and is much better. There's a significant difference.

Users are able to create deployments with Docker and TensorFlow. TensorFlow has a pre-trained model hub. It's a huge hub in a typical NLP or computer vision.

I've used TensorFlow in different areas within marketing tasks. For example, dynamic pricing solutions or classifications as to who will buy something or who will not buy something, or who will return. It's great to use in stock market scenarios, cryptocurrencies, foreign exchange markets, etc.

Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training. 

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. 

I've used the solution in the past 12 months. 

I haven't had any issues with stability so far. It's really reliable. There aren't issues with bugs or glitches or crashing.

The solution is absolutely scalable. My understanding of scalability is that when it comes to the solution, the learning task should run on the selected CPU. If I know how it should be and how it should be run, it's very easy as TensorFlow can also run on one CPU core or even on a GPU and so on.

Colab is great when I would like to learn something. You end up using Colab a lot. I like Jupyter Notebook and use it to create TensorFlow models.

There's also a lot of good documentation you can use to reference things and learn about the solution.

I have a bit of knowledge of PyTorch. I haven't used it much, however. I'm learning a bit about it now. While I don't have practical experience, I am taking a Coursera course that uses it.

In university, maybe ten years ago, I might have used something in MATLAB. I didn't have too much experience or too much knowledge about deep learning at that time, so I cannot say if it's hard or easy to do tasks in MATLAB or to compare the two. 

The initial setup isn't too complex. It's pretty straightforward. There is a lot of good documentation. There are many good courses. There are many good books from professors - from Ph.D.'s to data scientists. It's very, very easy. It's not so complex.

The deployment didn't take a very long time. In my experience, it only really took a few days. That is if a baseline model is enough for the client. Of course, if the requirement is an optimized model, it can be weeks or even a month.

The data processing, hyperparameter tuning, CPUs, and GPUs are all very, very important. If I have a very, very strong machine, I can do everything very fast, and it's a huge help for me.

I don't pay for the solution.

It's my understanding that if you want technical support you need to pay.

I'm just a user. I'm not a reseller or consultant.

I'm learning TensorFlow so I would like to do a TensorFlow certificate from Google in January or February. I'm learning how to deploy Poker with TensorFlow. It's new territory for me, however, it is very important.

I'm not sure which version of the solution I'm using. I have more developed servers and I'm using different versions.

I can recommend TensorFlow to anybody that wants to create deep learning models.

I'd rate the solution ten out of ten. I've been quite happy with it so far. 

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.

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.