Bring AI models to production


Train, validate, tune and deploy foundation and machine learning models, with ease

How it’s used


Diagram showing metrics of implemented AI


Watson Studio provides a collaborative platform for data scientists to build, train, and deploy machine learning models. It supports a wide range of data sources enabling teams to streamline their workflows. With advanced features like automated machine learning and model monitoring, Watson Studio users can manage their models throughout the development and deployment lifecycle.

Decision optimization

Diagram showing how to optimize decisions

Decision optimization

Decision optimization streamlines the selection and deployment of optimization models, and enables the creation of dashboards to share results and enhance collaboration.

Visual modeling

Diagram showing how to visually develop models

Visual modeling

With easy-to-use IBM® SPSS®-inspired workflows, you can combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI platform.

NLP with Watson

Screenshot of Watson Natural Language Processing Premium Environment

NLP with Watson

The Watson Natural Language Processing Premium Environment gives Watson Studio users instant access to pre-trained, high-quality text analysis models in over 20 languages. These models are created, maintained and evaluated for quality in each language by experts at IBM Research and IBM Software.

Automated development

Diagram showing how AutoAI helps speed development

Automated development

With AutoAI, beginners can quickly get started and expert data scientists can speed experimentation in AI development. AutoAI automates data preparation, model development, feature engineering and hyperparameter optimization.

AI governance

Product screenshot of AI governance dashboard

AI governance

AI governance automated tools and processes enable organizations to better direct, manage and monitor AI workflows. By tracing and documenting the origin of data, models, associated metadata and pipelines they are able to provide transparent and explainable analytic results. Operationalize AI effectively by managing risks, AI policies and regulations with custom workflows and dynamic dashboards.

IBM Watson Studio recognized as Leader in IDC's Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment

Flexible deployment options

Use our platform on various clouds

On IBM Cloud Pak for Data

Run Watson Studio on you own cloud using a unified data AI platform, including:

  1. Watson Studio (service)
  2. Cloud Pak for Data (platform)
  3. Red Hat® OpenShift® (architecture)
  4. Your private cloud (infrastructure)

On IBM Cloud Pak for Data as a Service

Run Watson Studio on a fully-managed data AI platform on IBM Cloud, including:

  1. Watson Studio (service)
  2. Cloud Pak for Data (SaaS platform)
  3. IBM Public Cloud (infrastructure)

We’re excited to announce the release of IBM Cloud Pak for Data version 4.6


Optimize AI and cloud economics

Icon representing Optimize AI and cloud economics

Put multicloud AI to work for business. Use flexible consumption models. Build and deploy AI anywhere.

Predict outcomes and prescribe actions

Icon representing Predict outcomes and prescribe actions

Optimize schedules, plans and resource allocations using predictions. Simplify optimization modeling with a natural language interface.

Synchronize apps and AI

Icon representing Synchronize apps and AI

Unite and cross-train developers and data scientists. Push models through REST API across any cloud. Save time and cost managing disparate tools.

Unify tools and increase productivity for ModelOps

Icon representing Unify tools and increase productivity for ModelOps

Operationalize enterprise AI across clouds. Govern and secure data science projects at scale.

Deliver explainable AI

Icon representing Deliver explainable AI

Reduce model monitoring efforts by 35% to 50%.¹ Increase model accuracy by 15% to 30%.² Increase net profits on a data and AI platform.

Manage risks and regulatory compliance

Icon representing Manage risks and regulatory compliance

Protect against exposure and regulatory penalties. Simplify AI model risk management through automated validation.

ESG validates Watson Studio capabilities

Report confirms ability to simplify and speed deployment of AI applications.


IBM Watson Studio - details

AutoAI for faster experimentation

Automatically build model pipelines. Prepare data and select model types. Generate and rank model pipelines.

Advanced data refinery

Cleanse and shape data with a graphical flow editor. Apply interactive templates to code operations, functions and logical operators.

Open source notebook support

Create a notebook file, use a sample notebook or bring your own notebook. Code and run a notebook.

Integrated visual tooling

Prepare data quickly and develop models visually with IBM SPSS Modeler in Watson Studio.

Model training and development

Build experiments quickly and enhance training by optimizing pipelines and identifying the right combination of data.

Extensive open source frameworks

Bring your model of choice to production. Track and retrain models using production feedback.

Embedded decision optimization

Combine predictive and prescriptive models. Use predictions to optimize decisions. Create and edit models in Python, in OPL or with natural language.

Model management and monitoring

Monitor quality, fairness and drift metrics. Select and configure deployment for model insights. Customize model monitors and metrics.

Model risk management

Compare and evaluate models. Evaluate and select models with new data. Examine the key model metrics side-by-side.

Trusted leadership

Read the reviews and product details on G2

Best Analytics and AI Products 2023 badge
Leader Winter 2023 badge
Momentum Leader Winter 2023 badge
Leader Small Business Winter 2023 badge

Product images

AI lifecycle automation

Screenshot showing relationship map and progress map

AI lifecycle automation

Explore relationships by building models with AutoAI.

Cloud, on-premises data sources

Screenshot showing multiple IBM and third-party data sources

Cloud, on-premises data sources

Access and select virtually any data source across clouds.

Drag-and-drop AI models

Screenshot showing GUI-based interface

Drag-and-drop AI models

Visually build models with an intuitive GUI-based flow.

Explain transactions for an AI model

Screenshot showing how you can change values for different predicted outcomes

Explain transactions for an AI model

Determine what new feature values would result in different outcomes.

What’s new

Hear the latest on Watson Studio

Listen to AI experts speak on best practices. Watch product demonstrations.

Get up to speed on AI governance

Explore what AI governance is, why it matters and how to make AI trustworthy.

Get started

Predict and optimize outcomes with AI and machine learning models.


¹,² New Technology: The Projected Total Economic Impact™ of Explainable AI and Model Monitoring in IBM Cloud Pak for Data, Forrester, August 2020.