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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
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IBM Cloud Pak for Data 5.0 is here with new features to streamline data sharing, data integration and governance

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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 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. Learn more about Data Science and MLOps
Decision optimization Decision optimization streamlines the selection and deployment of optimization models, and enables the creation of dashboards to share results and enhance collaboration. Learn more about Decision Optimization on cloud
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. Get started with IBM® SPSS® Modeler on Cloud
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. Get started with Watson NLP
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. Try AutoAI on cloud
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. Learn more about 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 Explore relationships by building models with AutoAI.
Cloud, on-premises data sources Access and select virtually any data source across clouds.
Drag-and-drop AI models Visually build models with an intuitive GUI-based flow.
Explain transactions for an AI model Determine what new feature values would result in different outcomes.
Case studies JPMorgan Chase

Improves model risk management with IBM Watson Studio.
Wunderman Thompson Data

Drives high-volume predictions with AutoAI.
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.