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TensorFlow pros and cons

Vendor: TensorFlow

4.4 out of 5

20 reviews
100% willing to recommend

Pros & Cons summary

TensorFlow offers an end-to-end package for data processing, model training, and deployment, backed by Google to ensure excellent documentation and community support. It excels in deep learning and building neural networks, although its integration capabilities are limited and requires significant computation power. While it allows development across diverse platforms, learning TensorFlow is complex with a steep curve. Issues include prototyping flexibility and hardware compatibility, especially with AMD GPUs on Macs.

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Prominent pros & cons

PROS

TensorFlow provides a transparent and end-to-end package for data processing, model training, evaluation, updating, and deployment without switching environments.

It is open-source, free, and supported by an extensive community and comprehensive Google documentation.

TensorFlow offers robust deep learning capabilities and efficiencies for building neural networks.

The framework is flexible, supporting both cluster computing and mobile devices, enabling diverse AI solutions.

TensorFlow's extensive documentation and easy implementation with inbuilt functions enhance its usability for various tasks.

CONS

TensorFlow lacks flexibility in prototyping compared to PyTorch.

Customization is challenging due to its C++ implementation and lacks efficient integration with JavaScript.

Heavy computational power is required, making local machine usage expensive and difficult especially without cloud resources.

There are difficulties integrating custom code and adapting to model tuning, with no model viewer for performance adjustment.

Users frequently face version mismatch errors, and the learning curve for new users is notably steep.

TensorFlow Pros review quotes

JB

Data Scientist at UpWork Freelancer

Nov 21, 2020

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, it improved the lives of many people around the globe.

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HL

AI Expert at lums

Nov 28, 2020

TensorFlow is a framework that makes it really easy to use for deep learning.

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BI

BEKKOUCHE Imad Eddine Ibrahim

Computer Vision Engineer at Innopolis University

Nov 17, 2020

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.

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GB

Machine Learning Engineer, AI Consultant at intelligentbusiness.hu

Jan 5, 2023

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

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GY

Machine Learning Engineer at Upwork

Dec 24, 2020

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

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Jan-Kees Buenen

CEO, co-Founder at SynerScope B.V.

Jul 14, 2023

What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device.

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JM

Managing Director at Geeky Bee AI

Nov 29, 2020

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

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Ashish Upadhyay

Founder at BlockMosiac

Nov 6, 2023

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

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reviewer1540461

Data Scientist at a university with 5,001-10,000 employees

Mar 29, 2021

It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models.

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AI

Project Manager at INFOCOM Ltd

Nov 29, 2020

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.

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TensorFlow Cons review quotes

JB

Data Scientist at UpWork Freelancer

Nov 21, 2020

However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I 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.

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HL

AI Expert at lums

Nov 28, 2020

JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow.

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BI

BEKKOUCHE Imad Eddine Ibrahim

Computer Vision Engineer at Innopolis University

Nov 17, 2020

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.

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824,067 professionals have used our research since 2012.

GB

Machine Learning Engineer, AI Consultant at intelligentbusiness.hu

Jan 5, 2023

It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models.

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GY

Machine Learning Engineer at Upwork

Dec 24, 2020

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.

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Jan-Kees Buenen

CEO, co-Founder at SynerScope B.V.

Jul 14, 2023

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

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JM

Managing Director at Geeky Bee AI

Nov 29, 2020

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.

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Ashish Upadhyay

Founder at BlockMosiac

Nov 6, 2023

For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort.

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reviewer1540461

Data Scientist at a university with 5,001-10,000 employees

Mar 29, 2021

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.

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AI

Project Manager at INFOCOM Ltd

Nov 29, 2020

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

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