Experience AutoAI

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

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IBM Watson Studio key features

Integrated automation of AI development

After training and saving a model, deploy AutoAI models while viewing the deployment details and test results.

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AutoAI model examples

Examine estimators, model types and metrics used by AutoAI, including algorithms trained on your data sets and feature transformations.

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Visual data science with IBM SPSS Modeler

With IBM SPSS® Modeler flows in IBM Watson Studio, you can quickly develop predictive models using business expertise.

Drag-and-drop models

Open source and IBM tool support

Bring popular tools, libraries and frameworks to deploy including Spark MLlib, scikit-learn, XGBoost, TensorFlow, SPSS Modeler, Watson Studio and so on.

Learn more about open source support

Visualize your data in Data Refinery

Identify patterns, connections, and relationships with the Data Refinery tool. Use smart chart options to speed your analysis.

Explore and refine data

Integrated visual recognition models

Use built-in visual-recognition models and analyze images for scenes, objects, and many other categories without any training.

Analyze images and build models

Broad data connection and tool support

Connect to data sources and choose the machine learning model that maps to the supported tools, algorithms and frameworks.

Add data to your projects

Extend Watson Studio with Decision Optimization models

Build Decision Optimization models as part of Watson Studio to combine prediction and optimization.

Predict and optimize

One-click model deployment with Watson Machine Learning

Deploy machine-learning algorithms to production at scale — across any cloud and on premises — with one click.

Learn more about one-click deployment

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

Problem

  • 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

Solution

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.

Explore the product tour

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Manage open-source notebooks as part of the AI lifecycle

Problem

  • 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

Solution

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

Problem

  • 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

Solution

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

Problem

  • 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

Solution

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

Problem

  • 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

Solution

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

Problem

  • 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

Solution

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 →

Learn six key tasks

Try a free hands-on lab for machine learning and deep learning with Watson Studio.

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Try or buy now

Explore data science and machine learning capabilities with Watson Studio.

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