TensorFlow Reviews

Name: TensorFlow
Brand: TensorFlow
Rating: 4.4 (19 reviews)

4.4 out of 5

19 reviews
100% willing to recommend

What is TensorFlow?

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

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TensorFlow is the #6 ranked solution in top AI Development Platforms. PeerSpot users give TensorFlow an average rating of 8.8 out of 10. Based on the analysis of the 19 most recent TensorFlow reviews, the overall sentiment is positive, with a sentiment score of 7.4. (Among the highest in the category). TensorFlow is most commonly compared to Microsoft Azure Machine Learning Studio: TensorFlow vs Microsoft Azure Machine Learning Studio. TensorFlow is popular among the large enterprise segment, accounting for 68% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a manufacturing company, accounting for 15% of all views.

Helped 816,660 peers since 2012

Featured reviews

Ashish Upadhyay

Founder at BlockMosiac

The one feature we find most valuable at our company is its robust and flexible machine-learning capabilities. 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. The ability to develop and fine-tune models, such as risk assessment for detection and market protection, as well as the creation of recommendation systems, is paramount. This versatility extends to providing personalized identity-relevant applications for our enterprise clients, delivering valuable insights to the market. Its exceptional support for deep learning and its efficient resource utilization enable us to undertake complex financial and data analyses. The flexibility it provides is crucial for meeting industrial requirements and crafting solutions tailored to our client's specific needs.

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Dan Bryant

Owner at II4Tech

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.

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RichardXu

Data Science Lead at a mining and metals company with 10,001+ employees

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.

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TensorFlow mindshare

As of November 2024, the mindshare of TensorFlow in the AI Development Platforms category stands at 5.7%, down from 9.8% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Last updated Nov 21, 2024

Valuable Features

"The available documentation is extensive and helpful."
"TensorFlow is an efficient product for building neural networks."
"It is easy to use and learn."

Room for Improvement

"The process of creating models could be more user-friendly."
"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."
"We encountered version mismatch errors while using the product."