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 a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale.
Watson Studio details
Embedded Watson tools
Train Watson with embedded AI services, including Watson Visual Recognition. Customize your models and deploy them as APIs 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 using Data Refinery. Understand the quality and distribution of your data with built-in charts and statistics.
Explore data and share compelling visualized results through interactive dashboards created within a powerful drag a 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.
Try Watson Studio today.
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.
Use your data to create, train, and deploy self-learning models. Leverage an automated, collaborative workflow to build intelligent applications.
Get started on Watson Studio in minutes
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