IBM named a Leader

Gartner releases 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Bring AI models to production 

How it’s used

Implement explainable AI

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Implement explainable AI

Explainable AI is a set of processes and methods that allows human users to comprehend and trust the results and output created by AI algorithms, including its expected impact and potential biases.

Optimize decisions

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Optimize decisions

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

Develop models visually

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Develop models visually

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.

Build ModelOps

IBM Cloud Pak for Data workflow, including collecting and preparing data, building and deploying AI models and optimizing decisions

Build ModelOps

ModelOps is a principled approach to operationalizing a model in apps. ModelOps helps you synchronize cadences between the application and model pipelines. You can optimize your AI and app investments from the edge to hybrid clouds.

Speed AI development with AutoAI

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Speed AI development with AutoAI

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.



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.

Product images

AI lifecycle automation

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AI lifecycle automation

Explore relationships by building models with AutoAI.

Cloud, on-premises data sources

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Cloud, on-premises data sources

Access and select virtually any data source across clouds.

Drag-and-drop AI models

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Drag-and-drop AI models

Visually build models with an intuitive GUI-based flow.

Explain transactions for an AI model

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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.

Synchronize AI and DevOps

Explore key capabilities for AI-led development and why you should integrate AI models into development cycles.

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.