What is Watson Studio?
IBM Watson Studio accelerates the machine and deep learning workflows required to infuse AI into your business to drive innovation. It provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build and train models at scale.
Watson Studio details
Embedded Watson tools
Train Watson with embedded AI services, including Watson Visual Recognition and Natural Language Classifier. Customize your models and deploy them as APIs with Watson Machine Learning, or CoreML.
Built on open source
Use familiar open source data science and machine learning tools, with Jupyter Notebooks with Anaconda and RStudio. Access the most popular libraries or bring your own.
Intuitive visual modeling
No-code visual modeling with Neural Network Modeler for designing neural architectures using the most popular deep learning frameworks. Train models easily with SPSS.
Easier data preparation
Interactively discover, cleanse, and transform your data with Watson Knowledge Catalog. Understand the quality and distribution of your data with built-in charts and statistics.
Interactive dashboards
Explore data and share compelling visualized results through interactive dashboards created within a powerful drag-and-drop authoring environment.
Elastic compute options
Easily scale your compute resources and customize your package dependencies to create a custom and reproducible environment that can be shared across your organization.
Customer success stories
Product Offering Images
Uncover hidden insights from data with built-in data cleaning and transformations with Data Refinery
Compare runs and conduct model hyper-parameter optimization easily with Deep Learning Experiments
Work in Customizable Environments to enable scalable compute with library management for reproducible research
Produce stunning visualizations directly from your data in real time, then quickly share using interactive Dashboards
Visually program for deep learning with an intuitive drag-and-drop, no-code interface in Neural Network Modeler
Uncover hidden insights from data with built-in data cleaning and transformations with Data Refinery
Use open-source tools like Jupyter Notebooks, combined with features like version control and programmatic access
Explore data and use machine learning with drag-and-drop tooling from SPSS Modeler - a new look on the classic tool
Improve your model's performance by visualizing fit between model and data, with Model Visualization within SPSS Modeler
Tutorials and use cases
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.
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.
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