Bring AI models to production
Scale AI across cloud environments
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 Watson Studio recognized as Leader in IDC's Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment
How it’s used
Implement MLOps and trustworthy AI
Implement MLOps and trustworthy AI
MLOps and trustworthy AI are 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
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
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.
Access Watson NLP
Access Watson NLP
The Watson Natural Language Processing Premium Environment gives Watson Studio users instant access to pre-trained, high-quality test 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.
Speed AI development
Speed AI 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.
Federated learning
Federated learning
With federated learning, train a model on a set of data sources from disparate sources without moving or sharing data. Each participating party in the federation trains the common machine learning model. The training results help improve model quality and accuracy with improved business insights while lowering risk from data security and privacy issues.
Flexible deployment options
Use our platform on various clouds
On IBM Cloud Pak for Data
Run Watson Studio on you own cloud using a unified data AI platform, including:
- Watson Studio (service)
- Cloud Pak for Data (platform)
- Red Hat® OpenShift® (architecture)
- Your private cloud (infrastructure)
On IBM Cloud Pak for Data as a Service
Run Watson Studio on a fully-managed data AI platform on IBM Cloud, including:
- Watson Studio (service)
- Cloud Pak for Data (SaaS platform)
- IBM Public Cloud (infrastructure)
We’re excited to announce the release of IBM Cloud Pak for Data version 4.6
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.
Features
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
AI lifecycle automation
Explore relationships by building models with AutoAI.
Cloud, on-premises data sources
Cloud, on-premises data sources
Access and select virtually any data source across clouds.
Drag-and-drop AI models
Drag-and-drop AI models
Visually build models with an intuitive GUI-based flow.
Explain transactions for an AI model
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.
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.
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.