IBM Watson Machine Learning vs TensorFlow comparison

IBM Watson Machine Learning

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and IBM Watson Machine Learning compete in the machine learning space. TensorFlow should be preferred for users seeking cost-effective solutions due to its comprehensive open-source features, while IBM Watson Machine Learning is superior for enterprises demanding extensive features and willing to invest more.

Features: TensorFlow offers flexibility in model building, a wide array of libraries for deep learning, and scalability for high-performance computations. IBM Watson Machine Learning provides automated model building, excellent natural language processing capabilities, and seamless integration with IBM Cloud.

Room for Improvement: TensorFlow could enhance its user interface for beginners, offer more comprehensive support for non-Python environments, and improve visualization tools. IBM Watson Machine Learning can refine its pricing structure, expand support for non-IBM cloud environments, and reduce complexity in setup and customization.

Ease of Deployment and Customer Service: TensorFlow is straightforward to deploy, particularly in Python-friendly environments, and benefits from a robust community for support. IBM Watson Machine Learning ensures a smooth deployment via IBM Cloud and offers dedicated customer support, providing a reliable environment for large enterprises.

Pricing and ROI: TensorFlow's open-source nature leads to minimal setup costs, appealing to budget-conscious developers seeking high ROI through custom implementations. IBM Watson Machine Learning entails higher initial costs but includes premium features and support, presenting significant ROI for enterprises ready to invest in comprehensive solutions.

To learn more, read our detailed IBM Watson Machine Learning vs. TensorFlow Report (Updated: January 2025).

IBM Watson Machine Learning vs. TensorFlow

January 2025

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Review summaries and opinions

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

Categories and Ranking

IBM Watson Machine Learning

Ranking in AI Development Platforms

11th

Average Rating

8.0

Reviews Sentiment

7.1

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 January 2025, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 2.2%, down from 2.6% compared to the previous year. The mindshare of TensorFlow is 4.5%, down from 8.9% 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.

Read full review

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

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

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

"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."

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

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

"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."

"TensorFlow is easy to implement and offers inbuilt functions for various tasks."

"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."

"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."

"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."

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

"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."

"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."

More TensorFlow pros

Cons

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

"Sometimes training the model is difficult."

"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."

"The supporting language is limited."

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

"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."

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

"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."

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

"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 currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."

"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."

"TensorFlow Lite only outputs to C."

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

More TensorFlow cons

Pricing and Cost Advice

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

"The pricing model is good."

"The solution is free."

"TensorFlow is free."

"We are using the free version."

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

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

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

"It is an open-source solution, so anyone can use it free of charge."

"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."

More TensorFlow pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

15%

Educational Organization

12%

Financial Services Firm

11%

University

11%

Manufacturing Company

14%

Computer Software Company

12%

University

10%

Educational Organization

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?

I am not familiar with the pricing setup cost and licensing.

What needs improvement with TensorFlow?

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...

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Product Reports

Download IBM Watson Machine Learning product report

AI Development Platforms

January 2025

Download TensorFlow product report

December 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