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

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Bring AI models to production 

Scale AI across any cloud

IBM Watson® Studio empowers data scientists, developers and analysts to build, run and manage AI models, and optimize decisions anywhere on IBM Cloud Pak® for Data. Unite teams, automate AI lifecycles and speed time to value on an open multicloud architecture.

Bring together open source frameworks like PyTorch, TensorFlow and scikit-learn with IBM and its ecosystem tools for code-based and visual data science. Work with Jupyter notebooks, JupyterLab and CLIs — or in languages such as Python, R and Scala.

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Build AI models with IBM Watson Studio
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Build AI models with IBM Watson Studio (01:13)

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.

Learn more about explainable AI

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.

Take the guided tour

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.

Read the Forrester report

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.

Get the in-depth insights

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.

Learn more about AutoAI

Flexible options

Build models where your data lives

Deploy on any cloud

On IBM Cloud Pak for Data

Run Watson Studio on your own hybrid cloud using a unified data and AI platform.

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On IBM Cloud Pak® for Data as a Service

Use a fully managed cloud environment that runs on IBM Cloud®, Amazon Web Services, Microsoft Azure or Google Cloud.

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On IBM Cloud Pak® for Data System

Choose a hybrid cloud, on-premises platform-in-a-box that combines storage, compute, networking and software.

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Benefits

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

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

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

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

Deliver fair, 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

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

Feature

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Dig deeper

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.

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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.

Client stories

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JPMorgan Chase

Case Study Preview Description

Improves model risk management with IBM Watson Studio

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Wunderman Thompson Data

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Drives high-volume predictions with AutoAI

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Highmark Health

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Monitors models to improve predictions

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FleetPride

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Transforms supply chain management with analytics

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Geisinger Health System

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Fights sepsis with machine learning

Read

What’s new

Hear the latest on Watson Studio

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

Watch on-demand webinars

Synchronize AI and DevOps

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

Read the Aberdeen study

Get up to speed on AI governance

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

Read the ebook

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Predict and optimize outcomes with AI and machine learning models.

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Footnotes

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