IBM named a Leader
Gartner releases 2021 Magic Quadrant for Data Science and Machine Learning Platforms.
Build, run and manage AI models
Scale AI across any cloud
IBM Watson® Studio empowers you to operationalize AI and optimize decisions anywhere on IBM Cloud Pak® for Data, the IBM data and AI platform. 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.
How it’s used
Speed AI development with AutoAI

Speed AI development with AutoAI
Implement explainable AI

Implement explainable AI
Optimize decisions

Optimize decisions
Develop models visually

Develop models visually
Flexible options
Build models where your data lives
Benefits
Optimize AI and cloud economics
Predict outcomes and prescribe actions
Synchronize apps and AI
Unify tools and increase productivity for ModelOps
Deliver fair, explainable AI
Manage risks and regulatory compliance
Feature
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
Cloud, on-premises data sources

Cloud, on-premises data sources
Drag-and-drop AI models

Drag-and-drop AI models
Explain transactions for an AI model

Explain transactions for an AI model
What’s new
Hear the latest on Watson Studio
Synchronize AI and DevOps
Get up to speed on AI governance
Related products
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Predict and optimize outcomes with AI and machine learning models.
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.