IBM watsonx.ai

Score8.8 out of 10

95 Reviews and Ratings

What is IBM watsonx.ai?

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.

Categories & Use Cases

Media

Screenshot of the foundation models available in watsonx.ai. Clients have access to IBM selected open source models from Hugging Face, as well as other third-party models, and a family of IBM-developed foundation models of different sizes and architectures.

Screenshot of the Prompt Lab in watsonx.ai, where AI builders can work with foundation models and build prompts using prompt engineering techniques in watsonx.ai to support a range of Natural Language Processing (NLP) type tasks.

Screenshot of the Tuning Studio in watsonx.ai, where AI builders can tune foundation models with labeled data for better performance and accuracy.

Screenshot of the data science toolkit in watsonx.ai where AI builders can build machine learning models automatically with model training, development, visual modeling, and synthetic data generation.

1 / 4

Screenshot of the foundation models available in watsonx.ai. Clients have access to IBM selected open source models from Hugging Face, as well as other third-party models, and a family of IBM-developed foundation models of different sizes and architectures.

Managed scaling
The platform provides the computing resources needed when they’re needed, allowing users to scale training and use up or down.
Category average: 6.4
Model deployment
After training is complete, models can be integrated into business applications using API endpoints or other convenient, standard methods.
Category average: 6.4
Security and compliance
End to end encryption, GDPR compliance, SSO, role-based permissioning, and other precautions are available to protect proprietary business data.
Category average: 6.4

Data management
Ingested data can be stored and prepped, with structures like data lakehouses available to handle large amounts of data from disparate sources.
Category average: 4.5
Data monitoring and version control
Teams can track which data is used in training at which point and roll back to previous versions as needed.
Category average: 4.5
Automated model training
After teams begin the process, model training can continue autonomously, enabling faster deployment.
Category average: 4.5

Most Frequent Users

Top 3 industries using IBM watsonx.ai.

Based on HG Insights installation data

View all Reviews

#1 most frequent

Professional, Scientific, and Technical Services

36.1%

22 installations of 61

“It has certainly sped up development.”
— IT Analyst, Infodive IT

#2 most frequent

Finance and Insurance

16.4%

10 installations of 61

“We had ample of time saving from automating manual data processing.”
— Systems Engineer, visa

#3 most frequent

Information

13.1%

8 installations of 61

“In our experience, IBM watsonx.ai capabilities train models in real time, which we could not do before.”
— software engineer, eia

Pros

Ease of use and effective prompting for AI interactions
Efficient custom model training with fast inference
Access to a foundational model catalog for various large language models

Cons

Challenges integrating with external and internal IBM tools
Cost concerns relative to competitor offerings
Insufficient comprehensive documentation and community support

Mitchell Beasley View profile

Marketing and Communications in Marketing at Siemens (10,001+ employees employees)

Use Cases and Deployment Scope

In my organization, I Love utilizing IBM watsonx.ai. Recommend to everyone!

Pros

I think the Fast results in IBM watsonx.ai are done well
In my experience, the Qualified data in IBM watsonx.ai is done well
Prompt

Return on Investment

Fast
KPI
Business

Alternatives Considered

AWS Chatbot

Verified User

Consultant in Information Technology (1001-5000 employees employees)

Use Cases and Deployment Scope

I use IBM watsonx.ai mostly as a model serving plattform. Occasionally, I use the prompt lab to help me evaluate multiple prompts for specific RAG (Retrieval-Augmented Generation) situations. Rarely, I get to use the ML features such as AutoAI for a quick modelling help. I enjoy the versatility .ai offers for various use cases.

Pros

Prompt Lab
Stability
Trustful Models

Cons

Interface is very divided
Main focus on Gen AI, make ML prominent as well
Easier hosting for custom APIs

Return on Investment

No custom API was negative
Enablement is complicated
Could easily create prompts for RAG

Other Software Used

IBM watsonx.governance, IBM watsonx.data, IBM watsonx Orchestrate

Kayla Brittney

Data scientist in Information Technology at Lawson Freights (1001-5000 employees employees)

Use Cases and Deployment Scope

We use IBM watsonx.ai to build, fine tune and deploy AI models that directly impact how we plan routes and manage fleet efficiency. We replaced our previous multiple standalone scripts that didn't communicate as well with a centralized IBM watsonx.ai environment.

Pros

Autoprompt and tuning studio
A built in governance checking system
Its efficiency at training custom models

Cons

It's currently so hard to visualize trends beyond basic plots
Integration with non-IBM ML frameworks is quite patchy

Return on Investment

Model retraining times have gone down, we can now refresh models twice as often as we used to
The model generated alerts reduce last minute reassignments

Other Software Used

IBM watsonx.data

Nikhil Jaitak View profile

DataScientist Lead in Research & Development at Emids technology (1001-5000 employees employees)

Use Cases and Deployment Scope

We use this to tune the llm, do prompting, deploy the models and then iterate for our multiple projects in medical domain like claims system, aba, chatbots etc. We are solving medical problems like medical record summarization, chatbot for QA from medical Record , ic code lookup and claims submission forms. It gives a good ecosystem of tools for miltiple usecases and all embedded within the same environment thats a great advantage and also using langchain is also great.

Pros

Integration with other systems
Deployment option within same exosyatem is great and can easily deploy any model .
Security layer for governing and hace insights to see the predictions and ai studio is also good

Cons

IBM watsonx.ai is expensive than other platforms.
Limited integraions though it has many but still some tools integrations not there for medical usecase
Its little difficult to learn as right now not many open reseouces
Community is not that strong to get any answer

Return on Investment

For one of the usecase we were able to save 1.3 million dollar by creating the COB project in 2 months by leveraging the whole ecosystem and savibg of approximately 2800 hrs per annum
For the icd usecase wherre we have used the graph memgraph using docker to deploy it and created graphs and then created inference pipeline saved 20 minutes per medical coder and approximately of 3.5 million dollar savings as claims submission verifying on the payer side is a lengthy process

Other Software Used

IBM Watson Natural Language Understanding, IBM Watson Speech to Text, IBM Watson Visual Recognition (discontinued)

Harshal Pachpande View profile

Analyst in Information Technology at Secureview (501-1000 employees employees)

Use Cases and Deployment Scope

In our orgnisation IBM watsonx.ai is primarily used to enhance threat detection automate security analytics and it helps to improve accuracy of incident triage within our soc Operations as security analyst I leverage this platform to analyze large volume of log and alert data to interpret them against threats and malwares or malicious behaviour from Qradar logs

Pros

AI driven LOG ANALYST and Investigation processing large volume of alerts and security events for incident categorisation and its identification that helps to detect attac at earlier stages of security incidents

Cons

while AI driven insights are accurate the reasoning behind alert prioritization or anomalu scoring is somtimes opaque analyst often need more transperancy into why specific event was flagged or how a confidance score was derived

Return on Investment

Significant time saving in alert triage and investigation
Improved accuracy and reduced false detections

Gemini Enterprise Agent Platform

Amazon Bedrock

IBM watsonx.ai

What is IBM watsonx.ai?

Categories & Use Cases

Media

IBM watsonx.ai Key Features

Top Performing Features

Managed scaling

Model deployment

Security and compliance

Areas for Improvement

Data management

Data monitoring and version control

Automated model training

Who Buys & Uses IBM watsonx.ai

Most Frequent Users

Professional, Scientific, and Technical Services

Finance and Insurance

Information

Most Frequent Users

Small Businesses

Mid-sized Companies

Enterprises

Most Frequent Users

United States of America

Canada

Brazil

Most Frequent Users

Engineering

Marketing

IT

IBM watsonx.ai Reviews

Pros

Cons

Use Cases and Deployment Scope

Pros

Return on Investment

Alternatives Considered

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

IBM watsonx.ai Alternatives