IBM Reviews, Ratings & Features 2026 | Gartner Peer Insights

Gartner defines AI platforms for data science and machine learning (DSML) as platforms that support end-to-end AI model and agent development and life cycle management using diverse data science and AI techniques. These platforms enable data preparation, model building, deployment and governance, and are delivered as fully managed cloud services or on-premises infrastructure for AI experts and business users.

AI platforms for DSML primarily target AI experts, including data scientists and AI engineers, providing a comprehensive suite of data science and AI techniques for augmented and automated decision making. These platforms support building models using data-intensive methods such as machine learning, as well as techniques such as simulation and optimization. They handle all types of data — tabular, image, video and text — for multimodal applications, such as computer vision, natural language processing (NLP) or composite AI, that combines optimization with machine learning.

Active

Find the top AI Platforms for Data Science and Machine Learning with Gartner. Compare and filter by verified product reviews and choose the software that’s right for your organization.

AI Platforms for Data Science and Machine Learning

IBM SPSS Modeler

IBM Data Science Experience (DSX) (Legacy)

IBM watsonx

IBM watsonx.ai

Vp, It Security And Risk Management

Great　analytics　tool　to　create　PA　models　very　easily,　and　it　can　be　connected　with　other　applications / cloud / systems in a minutes

<50M USD

50M - 250M USD

30B + USD

250M - 500M USD

3B - 10B USD

Gov't/PS/ED <5,000 Employees

500M - 1B USD

1B - 3B USD

10B - 30B USD

Gov't/PS/ED 5,000 - 50,000 Employees

Gov't/PS/ED 50,000 + Employees

R&D Engineer

Overall Experience has been good as it provides suitable tools for the team to collaboratively work on models and scale them with big datasets. It is able to predict and analyze the data to get patterns.

IT Services Associate

I have been using IBM watsonx.ai with my team as part of a broader enterprise AI initiative. Overall, it's been a solid platform for taking models beyond experimentation and into production. What I like most is that governance, security, and deployment are built into the platform. There is a learning curve, especially for teams new to AI platforms, but once the structure is understood, it significantly reduces long-term operational risk.

Microsoft

Amazon Web Services (AWS)

Google

Oracle

Other...

RapidMiner

Alteryx

Salesforce (Tableau)

Anaconda

Accenture

Databricks

Cloudera

BigML

Teradata

Dataiku

FICO

KNIME

Strategy

Qlik

MathWorks

Pentaho

TIBCO

Zendesk

Tungsten Automation

H2O.ai

DataRobot

IBM is a well-established entity focused on technology and development. The primary mission revolves around fostering technological growth and enhancing infrastructure, achieved through focused developments and consulting services. By encouraging inventiveness and innovation, it is geared towards facilitating the transition of theoretical ideas into practical realities, thus improving global functionalities. IBM brings about transformation by creating advanced solutions that reshape and redefine the world.

At IBM, we do more than work. We create. We create as technologists, developers, and engineers. We create with our partners. We create with our competitors. If you're searching for ways to make the world work better through technology and infrastructure, software and consulting, then we want to work with you.

We're here to help every creator turn their "what if" into what is. Let's create something that will change everything.

Reviews

Ratings Overview

Overall experience with

Favorable Review

Solid enterprise AI platform to move teams from experimentation to production.

Critical Review

Advantages and Disadvantages of IBM Watson studio!

Likes and dislikes about

likes

dislikes