ESG validates Watson Studio capabilities
Report confirms ability to simplify and speed deployment of AI applications.
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.
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 with AutoAI
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.
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 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
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.
Synchronize AI and DevOps
Explore key capabilities for AI-led development and why you should integrate AI models into development cycles.
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.