Experience AutoAI

Take a quick tour of IBM Watson® Studio and see why AutoAI is an award-winning feature.

IBM Watson Studio key features

Get started with faster data preparation and model building

Prepare and refine data anywhere, anytime

See data preparation in Watson Studio Desktop (06:00)

Get started with data science. Prepare, explore and refine data in Watson Studio Desktop.

Automate model building with AutoAI

Try AutoAI tutorial for customer churn

Automatically build models, view a leaderboard, compare pipelines and deploy selected models with Watson Studio and Watson Machine Learning.

Predict a product purchase

Learn how to automate prediction

Predict whether a customer is likely to buy a tent from an outdoor equipment store with AutoAI using Watson Studio and Watson Machine Learning.

Get more from your unstructured data

Watch text analytics in action

Turn email, support logs and other textual information into insight and prediction by using the powerful text analytics function in SPSS Modeler in Watson Studio Desktop.

Build a product recommendation engine

Learn how to build the engine

Use Jupyter Notebooks with IBM Watson Studio to build an interactive recommendation engine PixieApp and deploy it with Watson Machine Learning.

Use a Python notebook to deploy decision optimization

See how decision optimization gets deployed (05:04)

Deploy your decision optimization model with Watson Machine Learning, using a Jupyter notebook to access machine learning services and monitor jobs.

Watson Studio use cases

Get started with data science using AutoAI


  • It takes weeks or months for data scientists to achieve good prediction and monitor models in production
  • Data scientists who code and know algorithms are in short supply
  • Citizen data scientists and analysts need a rapid onramp


AutoAI helps data scientists to rapidly develop candidate pipelines, select top performing models on the leaderboard and deploy models with Watson Machine Learning. This process makes model monitoring easier and faster, reducing the process to hours and minutes.

Try AutoAI tutorial →

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Get insights from your data

Create and test a visual recognition model.

Young woman in office examining model building

Manage open-source notebooks as part of the AI lifecycle


  • Businesses are concerned with lack of governance in open source-based models
  • Visibility and understanding of model deployment status is difficult to acquire
  • It’s a challenge to share outcomes with analysts and SMEs


With Watson Studio, you can bring your own open-source codes into an environment with the security, governance and scalability that your enterprise needs.

Learn from Digital Technical Engagement webinars  (link resides outside IBM) →

Deploy projects faster with an expanded talent pool


  • There are not enough data scientists with required skill sets and understanding of business domains
  • It’s difficult to get started with data science projects and to train users quickly
  • You want to have the ability to deploy models with a governed machine learning approach in the future


SPSS Modeler in Watson Studio is a visual data science tool that enables anyone to prepare data and build models, without coding experience.  You can also use Watson Machine Learning when you are ready.

Explore Watson Studio Desktop →

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Drone flying in an orange grove

Get insights and build models with images and videos


  • You want to get started by using pre-built visual recognition rather than by building a custom model
  • You don’t want to invest in system infrastructures for deep learning until you show value
  • You need to use your visual recognition models with other models and data assets across multiple clouds


IBM Watson Visual Recognition service analyzes images for scenes, objects and other content. IBM Watson Studio provides a collaborative environment in the cloud where you can work with your images and your visual recognition custom models.

Build a visual recognition classifier →

Use predictive models to optimize your decisions


  • You’re unable to deploy outcomes from a range of scenarios for a business problem
  • It’s a challenge to run optimization models using spreadsheets and point tools
  • You want to blend machine learning and decision optimization using the same deployment mechanism


IBM offers an industry-focused approach to combine predictive and prescriptive models to optimize scheduling, resource allocations and supply and demand matching.

View the ESG analyst report →

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Pilot in jet cockpit adjusting controls

Build models for AI-powered apps with a data and AI platform


  • Your data science practice has not caught up with the agile practice of DevOps and app development
  • It’s challenging to adapt to users and stakeholders with varying skills and backgrounds
  • Your plans to bring prediction and optimization into your app have been delayed or stalled


IBM Watson Studio Premium for IBM Cloud Pak™ for Data provides a seamless experience for people at any skill level to power your prediction and optimization needs. It helps you uniquely leverage data and AI across any cloud–public clouds, private clouds or any combination.

Learn more about Watson Studio Premium →

IBM named a Leader

Gartner releases 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Try or buy now

Explore data science and machine learning capabilities with Watson Studio.