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Watson Studio

 IBM Watson Studio
Build trust and scale AI across cloud environments
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Screenshot showing IBM Watson Studio dashboard with pipelines and top algorithms
Bring AI models to production

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.
IBM unveils Data Product Hub to enable enterprise-wide data sharing
Announcement
IBM Cloud Pak for Data 5.0 is here with new features to streamline data sharing, data integration and governance Learn why your organizations need explainable AI and why it matters
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Now available: watsonx.ai

Announcing the launch of watsonx.ai - The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models.

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How it’s used

MLOps Decision optimization Visual modeling NLP with Watson Automated development AI governance
Begin your journey to AI
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 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.
ESG validates Watson Studio capabilities. Report confirms ability to simplify and speed deployment of AI applications
IBM Watson Studio - Details Learn more 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.
IBM Watson Studio recognized as Leader in IDC's Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment
Trusted leadership
Leader in G2 Fall 2023 Grid Reports for Data Preparation, Data Science and Machine Learning Platforms, MLOps Platforms, Predictive Analytics and Text Analysis. Read the infographic

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AI lifecycle automation Cloud, on-premises data sources Drag-and-drop AI models Explain transactions for an AI model
Case studies

Improves model risk management with IBM Watson Studio.
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Highmark Health

Monitors models to improve predictions.
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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.