Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.

Data Science and Machine Learning Platforms

Gartner defines the market of AI in communications service provider (CSP) customer and business operations as commercial off-the-shelf (COTS) products. They are either capabilities embedded in CSP-specific operational technology (OT) applications (such as channels, CRM and other business support system [BSS] applications) or industry-agnostic horizontal applications delivering AI/machine learning (ML)-based customer and business operations in CSPs. CSP customer and business operations refer to marketing, sales, customer acquisition, customer journey, billing and revenue management, revenue assurance, and related risk management. The scope of AI products covers data readiness, life cycle management of algorithms and their application to CSP customer and business operations.
AI in CSP customer and business operations helps CSPs utilize AI/ML to generate insights and automate business processes across a wide range of customer and business operations areas. These areas include marketing, sales, configure-price-quote (CPQ), order management, product management, billing and revenue management, customer journey and care, revenue assurance, and fraud/risk management. An example can be intelligent chatbots utilizing natural language processing for customer interaction use cases to automate call center operations. These products assist with insights and automation and help CSPs manage the life cycle of AI/ML algorithms, gradually enabling ModelOps/AIOps.

AI in CSP Customer and Business Operations

Gartner defines cloud AI developer services (CAIDS) as cloud-hosted or containerized services and products that enable software developers who are not data science experts to use artificial intelligence (AI) models via APIs, software development kits (SDKs) or applications. Core capabilities include automated machine learning (autoML) including automated data preparation, automated feature engineering and automated model building, and model management and operationalization for language, vision and tabular use cases. Optional and important complementary capabilities include AI code models and assistants.
Cloud AI developer services help organizations embed intelligence, such as AI and ML insights, into their applications. While that is what cloud AI developer services offer, it is more important to note how they accomplish this. These services democratize and increase the availability of AI and ML to software engineers through the automation and features offered. Traditional activities regarding data acquisition, data quality, feature engineering, algorithm selection and model training are augmented by the technology. Cloud AI developer services open up a world of possibilities for software engineers to build AI and ML production capabilities and features for enterprise-built applications.

Cloud AI Developer Services

Generative AI (GenAI) apps use generative AI capabilities for user experience and task augmentation to accelerate and assist the completion of a user’s desired outcomes. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. When embedded in the experience, generative AI offers richer contextualization for singular tasks such as generating and editing text, code, images and other multimodal output. As an emerging capability, process-aware generative AI agents can be prompted by users to accelerate workflows that tie multiple tasks together. Apart from helping save time and money, generative AI apps help improve branding of businesses by creating more engaging and effective content while also creating more engaging and immersive experiences for customers. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.

Generative AI Apps (Transitioning to AI Knowledge Management Apps/ General Productivity)

Generative AI Apps

Generative AI (GenAI) engineering refers to the field of engineering that focuses on the development, implementation and optimization of generative AI models. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. By developing GenAI models, engineers can create new and innovative ways to generate content. The vendors in this segment are made up by incumbent and startup vendors covering full-model life cycle management, specifically adjusted to and catering to development, refinement and deployment of generative models (e.g., LLMs) and other GenAI artifacts in production applications. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.

Generative AI Engineering

IBM watsonx Reviews, Ratings & Features 2025 | Gartner Peer Insights

Evaluation and contract negotiation

Evaluation & Contracting

Ability to understand your organization's needs

Timely and complete response to product questions

Pricing and contract flexibility (pricing and terms)

Integration and deployment

Integration & Deployment

Availability of quality 3rd-party resources (integrators, service providers, etc.)

Ease of integration using standard APIs and tools

Quality and availability of end-user training

Ease of deployment

Service and support

Service & Support

Timeliness of vendor's response

Quality of technical support

Quality of peer user community

Product capabilities

Product Capabilities

Data Access and Manipulation

Data Exploration and Visualization

Predictive Analytics

Forecasting

Optimization

Simulation

Delivery, Integration & Deployment

Platform and Project Management

Performance and Scalability

User Experience

Data Access

Data Preparation

Augmentation (Automation)

User Interface

Machine Learning

Other Advanced Analytics

Flexibility and Openness

Delivery

Model Management

Pre-canned Solutions

Collaboration

Coherence

Planning and Design

Data Science Techniques

Notebook Development

Model Consumption

Data Pipelines

Feature Management

Experiment Tracking

Deployment and Serving

Monitoring and Observability

GenAI Models

Scalability

Additional Context

Dataiku

DataRobot Enterprise AI Suite

DataRobot

Amazon SageMaker AI

Amazon Web Services (AWS)

Alteryx Designer

Alteryx

MATLAB

MathWorks

Altair RapidMiner

Siemens (Altair)

IBM SPSS Statistics

Databricks Data Intelligence Platform

Databricks

Anaconda AI Platform

Anaconda

Base SAS

<50M USD

50M - 250M USD

3B - 10B USD

30B + USD

250M - 500M USD

10B - 30B USD

500M - 1B USD

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

1B - 3B USD

IBM watsonx

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.

SOFTWARE DEVELOPER

The IBM Watsonx platform is very easy to work with, even for those not deeply technical, and it has helped me save time by automating tasks that used to take longer.
Collaboration features, shared workspaces and real-time updates make team work much easier.
Mostly I like the machine learning and AI features.
Watsonx platform having built-in machine learning models that can be used for real time projects directly for integration and deployment.
Within one platform it has Data, AI, and Govern features for data analysis and prediction for feature.
AI engineers and data scientists can use this platform to Build machine learning models, develop foundation models and manage the AI lifecycle to train, validate and deploy AI models in an easy way.
Evaluate model output to help select the champion model or the best model out of all models.
Creating chatbots is very easy and effective
Create prompts to generate, summarize, and extract meaning from your unstructured data.

Microsoft

Google

Oracle

Other...

Salesforce (Tableau)

Accenture

BigML

Cloudera

Qlik

RapidMiner

Zendesk

Tungsten Automation

BigSquid AI

KNIME

Pentaho

Teradata

H2O.ai

Strategy

Salesforce

TEAM LEAD

analytical and data processing  tool with limited features and available only in one language 

Product Program Manager

Watson has always been and always will be vapor ware. It's tragic and sad to see how far the company has fallen and how quickly they've been outpaced by AWS and Azure ML. 

IT Specialist

IBM Watson is a rebrand of the popular SPSS statistics. The product support different technologies to develop and deploy machine learning model.

Lead- Business Development

IBM Watson is one of the most advanced platforms to build Artificial Intelligence applications. Its tricky and is advanced.

ENGINEER

Out of the box has a lot of useful functionality. Administratively not too many controls as it's managed by the central IBM IAM.

NETWORK ENGINEER

i like it because of it can provide a smooth and collaboratively work with our data in my enviroment.

System Engineer

With IBM Watson Studio, AI and ML can be implemented quickly and easily.

PRE SALES

it is a platform that helps to better interpretthe analysis of company data and thus make better decisions 

Active

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50M-1B USD

1B-10B USD

10B+ USD

Gov't/PS/Ed

BUSINESS INTELLIGENCE, BIG DATA AND BURE

supplier always avaiable to talk and the cms figure supports us in using the tool in the best way possible.

Data Scientist

IBM watson studio provides me with the perfect collection of tools to collaboratively work with large datasets in real time alongside my team-mates,  model algorithms for  optimized decision purposes and develop predictive models  which are essential for efficient statistical analysis , node text analysis  and so on.
 In summary,  Watson Studio just allows my organization operationalize data science at scale.

ANALYTICS MARKET MANAGER

For our consumer studies we needed to perform advanced segmentation based on respondent's profile and specific buying habits. Data was collected with Qualtrics XM platform and Watson Studio served as data-mining tool. Data scientists assigned to the project could efficiently collaborate to compare models and discuss predictions. This methodology worked with success and had a very positive impact on the team spirit.

Creative Manager

IBM Watson Studio provides various tools for natural language processing and creating a chatbox. The chatbox development platform is easy to use and allows one to create an AI-based chat bot with a natural language interface. It offers an intuitive drag and drop interface that allows you to design and develop the chat bot without any coding or computer programming experience. 

Marketing Consultant

The product makes it efficient to build and scale trusted AI.

Deal Desk Manager

IBM Watson Studio is a great tool for data science and artificial intelligence. The services available here are great and ensures end to end delivery.

BIOINFORMATICIAN

This platform, from my experience is very robust. I actually got to hear about it through a free Machine Learning course I took on IBM's cognitive class platform. More tutorials on the features should be made available as the site is not very straightforward for a novice. All in all I am loving the platform by the day.

Business Intelligence Analyst

Watson Machine Learning has enabled the deployment of complex solutions that leverage predictive and prescriptive Analytics in my company, allowing the generation of value across different business operation areas.

We solve intelligent schedule problems with Waston Studio, we leverage decision optimization engine with Python API to build the solution. The models are deployed with rest API to implement automated planning and scheduling decision

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.

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