IBM Watson Machine Learning vs TensorFlow comparison

IBM Watson Machine Learning

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Executive Summary

We performed a comparison between IBM Watson Machine Learning and TensorFlow based on real PeerSpot user reviews.

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed IBM Watson Machine Learning vs. TensorFlow Report (Updated: October 2024).

IBM Watson Machine Learning vs. TensorFlow

October 2024

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Categories and Ranking

IBM Watson Machine Learning

Ranking in AI Development Platforms

11th

Average Rating

8.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

TensorFlow

Ranking in AI Development Platforms

6th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 2.6%, up from 2.6% compared to the previous year. The mindshare of TensorFlow is 5.7%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Anurag Mayank

Manager at Maruti Suzuki India Limited

A highly efficient solution that delivers the desired results to its users

I had not considered how the solution could be improved because I was focused on how it was helping me to solve my issues. If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use. It would be beneficial to incorporate more AI into the solution.

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Ashish Upadhyay

Founder at BlockMosiac

A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface

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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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It is has a lot of good features and we find the image classification very useful."

"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."

"It has improved self-service and customer satisfaction."

"The most valuable aspect of the solution's the cost and human labor savings."

"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."

"Scalability-wise, I rate the solution ten out of ten."

"I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive."

"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."

"The available documentation is extensive and helpful."

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

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

"It's got quite a big community, which is useful."

"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."

"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."

"It is easy to use and learn."

More TensorFlow pros

Cons

"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."

"In future releases, I would like to see a more flexible environment."

"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."

"The supporting language is limited."

"Sometimes training the model is difficult."

"If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use."

"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."

"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."

"The process of creating models could be more user-friendly."

"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."

"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. It’s more like plumbing as opposed to a fully automated environment."

"We encountered version mismatch errors while using the product."

"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."

"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."

"The solution is hard to integrate with the GPUs."

More TensorFlow cons

Pricing and Cost Advice

"The pricing model is good."

"I've only been using the free tier, but it's quite competitive on a service basis."

"We are using the free version."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"The solution is free."

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

"TensorFlow is free."

"I rate TensorFlow's pricing a five out of ten."

"I am using the open-source version of TensorFlow and it is free."

More TensorFlow pricing and cost advice

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816,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

14%

University

14%

Financial Services Firm

12%

Educational Organization

12%

Manufacturing Company

15%

Computer Software Company

12%

University

10%

Educational Organization

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about IBM Watson Machine Learning?

I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive.

What is your experience regarding pricing and costs for IBM Watson Machine Learning?

The solution is expensive.

What needs improvement with IBM Watson Machine Learning?

Sometimes training the model is difficult. We need to have at least a few different components to evaluate and understand the behavior of different users to have a very, very high accuracy in the m...

What do you like most about TensorFlow?

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.

What is your experience regarding pricing and costs for TensorFlow?

The solution is free.

What needs improvement with TensorFlow?

The process of creating models could be more user-friendly.

Google Cloud AI Platform vs IBM Watson Machine Learning

Comparisons

Compared 65% of the time

Azure OpenAI vs IBM Watson Machine Learning

Compared 20% of the time

IBM Watson Studio vs IBM Watson Machine Learning

Compared 4% of the time

More IBM Watson Machine Learning Competitors

Microsoft Azure Machine Learning Studio vs TensorFlow

Compared 27% of the time

Google Vertex AI vs TensorFlow

Compared 24% of the time

OpenVINO vs TensorFlow

Compared 19% of the time

Hugging Face vs TensorFlow

Compared 8% of the time

PyTorch vs TensorFlow

Compared 5% of the time

More TensorFlow Competitors

Product Reports

Download IBM Watson Machine Learning product report

AI Development Platforms

November 2024

Download TensorFlow product report

October 2024

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IBM

Overview

IBM Watson Machine Learning helps data scientists and developers accelerate AI and machine-learning deployment. With its open, extensible model operation, Watson Machine Learning helps businesses simplify and harness AI at scale across any cloud.

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.

Sample Customers

Information Not Available

Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm