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IBM Watson Studio Desktop

Product by IBM

Prepare and build models on your desktop with visual drag and drop tools. Easy data science on your Mac or PC.

Available plans:

IBM Watson Studio Desktop 30-Day Trial

IBM Watson Studio Desktop - Monthly

Build and train AI models, and prepare and analyze data, in a single, integrated environment

  • Prepare, blend, explore, and model your data within minutes without coding
  • Unlimited modeling without overages
  • Open source integration supports up to 2 weeks of offline use
  • Easily switch between deployment offerings with the same designs as Watson Studio Cloud and Local
  • Includes most popular Watson Studio tools including SPSS Modeler, Data Refinery, Projects, and a convenient check for updates and features
Data preparation
Uncover hidden insights in your data with built-in data cleaning and transformations with Data Refinery. Have access to a tabular view of your data, including visualizations and summary statistics, which help you to uncover hidden insights by asking the right business questions of your data.

Explore the data science lifecycle

Data exploration
Using the included dashboarding service, produce stunning visualizations directly from your data in real time, allowing you to illuminate previously unknown patterns, relationships or other actionable findings. Then easily share with your team.

Create and test a visual recognition model

Model development
Test and deploy models, using customizable compute environments that scale up and down with your workflow. Choose the best environment based on the phase of your model and the scale needed. Choose from various capacities of Anaconda, Spark and GPU environments.

Try the hands-on lab to create deep learning models

Model evaluation
Improve your model's performance by visualizing fit between model and data, with Model Visualization within SPSS Modeler.

Walk through the process of building a machine-learning model

Model deployment
Once your model is ready, deploy and score your model with the available IBM Watson Machine Learning service.

Watch the model deployment video

Model management
Compare runs and conduct model hyper-parameter optimization easily with Deep Learning Experiments.

Watch the model management video

Communities and Documentation

Data science community
Harness the power of the community. Check out the collection of articles, data sets, notebooks and tutorials.

Explore the community

Github repository
A repository of demos, tutorials, sample apps, and more.

Check out the resources

Forrester study
Forrester Consulting’s Total Economic Impact study quantifies the benefits of deploying Watson Studio and Watson Knowledge Catalog.

Download the study

Stack Overflow
Commons questions and answers about Watson Studio.

Search for answers

Documentation
Explore more of what you can see and do with Watson Studio and see videos of key tasks.

Read documentation

Solution brief
Find out how you can use this integrated platform designed to help data scientists and business analysts develop, train and manage models.

Read more

Demos

Introduction to IBM Watson Studio Desktop
See how to start to prepare data and build models on your desktop with visual drag and drop tools.

Watch the video (6:56)

Neural network modeler and deep-learning experiments
Get an overview of the neural network modeler tool and see how quickly a model can be created using this tool.

Watch the video (3:05)

Model management and deployment
Watson Studio allows data scientists and analysts to quickly build and prototype models, monitor deployments and learn over time, as more data becomes available.

Watch the video (2:49)

Build a logistic regression model
See how easy it is to build a logical regression model using the tooling available in Watson Studio.

Watch the video (4:03)

Watson Studio Local - Administration
See the introduction of Watson Studio Local administrative dashboard and community features.

Watch the video (5:39)

Watson Studio Local - Analytics
See the introduction of Watson Studio Local model builder and analytic asset features.

Watch the video (6:28)

Product Tours and Hands-on Labs

IBM Watson Studio product tour: Create a machine-learning model to predict customer churn
Get experience with Watson Studio by creating a decision-tree machine-learning model to evaluate the risk of a customer leaving your service.

Explore the product tour

IBM Watson Studio: Machine learning and deep learning made easy
Learn how to pick the best model for churn prediction and take advantage of Watson services.

Try the hands-on lab

IBM Watson Studio Modeler Flows product tour: Create an SPSS machine-learning model
Use Watson Studio Modeler Flows and the SPSS runtime to create a machine-learning model that evaluates customer churn risk and programmatically scores records.

Explore the product tour

Speed up machine-learning and deep-learning development with modeler flows
Learn how to create Modeler Flows for three runtime environments - SPSS, Spark, and Spark for Neural Networks.

Try the hands-on lab

IBM Watson Studio product tour: Create and test a visual recognition model
This product tour demonstrates how easy it is for all enterprises, big and small, to access visual recognition.

Explore the product tour

IBM Watson Studio Local product tour: Build, train and deploy machine-learning models without coding
Train and deploy a machine-learning model with this product tour, without any coding or manual model building skills.

Explore the product tour

Build, deploy, test, and retrain a predictive machine learning model
This tutorial walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.

Go to the tutorial

Analyze and visualize open data with Apache Spark and Watson Studio
In this tutorial, you will analyze and visualize open data sets using a Jupyter Notebook on IBM Watson Studio and Apache Spark service for processing. For this use case, you will start by combining data about population growth, life expectancy and country ISO codes into a single data frame. Then, query and visualize that data in several ways using the Pixiedust library for Python.

Go to the tutorial

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