Watson Studio

Simplify and scale data science to predict and optimize your business outcomes

IBM's integrated hybrid environment that provides flexible data science tools to build and train AI models and prepare and analyze data.

Tutorials

28 February 2025

Tutorial

Streamlining data and Machine Learning workflows with Watsonx Studio Pipelines

Learn to automate data processing pipelines in Watson Studio, optimize machine learning workflows, and integrate with IBM Cloud Object Storage for scalable solutions.

26 June 2023

Tutorial

Use Watson Pipelines to build model pipelines

Get a better understanding of how to orchestrate an AutoAI experiment by using Watson Pipelines.

03 April 2023

Tutorial

Generate machine learning model pipelines to choose the best model for your problem

Learn more about AutoAI, a service that automates machine learning tasks, such as automatically preparing your data for the modeling, choosing the best algorithm for your problem, and creating pipelines for the trained models.

08 November 2022

Tutorial

Serve a custom model on a Kubernetes or Red Hat OpenShift cluster

Learn how to take a Watson NLP model that you trained in IBM Watson Studio and serve it on a Kubernetes or Red Hat OpenShift cluster.

27 May 2022

Tutorial

Orchestrating end-to-end machine learning workflows with Watson Pipelines

Learn how to create an automated pipeline that can take your existing data to train multiple models with hyperparameter optimization using AutoAI, then deploy the best-selected model into production on a web service using the Watson Machine Learning service.

23 August 2021

Tutorial

Build a recurrent neural network using Pytorch

Learn the basics of how to build an RNN by using a Jupyter Notebook written in Python, IBM Watson Studio, and IBM Cloud Pak for Data.

21 July 2021

Tutorial

Implement autoencoders using TensorFlow

Learn about basic autoencoder concepts using TensorFlow, then implement them on a Jupyter Notebook.

16 July 2021

Tutorial

Automate model building with AutoAI

This tutorial explains the benefits of the AutoAI service on a Credit Risk use case so you can have a better understanding of how regression and classification problems can be handled without any code and how the tasks (feature engineering, model selection, hyperparameter tuning, etc.) are done with this service. The tutorial also includes details for choosing the best model among the pipelines and how to deploy and use these models via IBM Cloud Pak for Data platform.

18 May 2021

Tutorial

Predict future product demand using SPSS Modeler

Create a predictive machine learning model to help you determine your optimal inventory level.

10 March 2021

Tutorial

Build a recurrent neural networks using TensorFlow Keras

Use a Jupyter Notebook to create an RNN model based on the LSTM unit to train and benchmark on the Penn Treebank data set, and learn how TensorFlow builds and executes an RNN model for language modeling.

30 November 2020

Tutorial

Optimize plant selection based on cost and capacity with Decision Optimization

Set up a decision optimization problem, import data, set objectives and constraints, and then run the model.

25 March 2020

Tutorial

Collect, cleanse, and enhance your data

In this tutorial, we demonstrate how data scientists can easily collect data from databases, analyse the data and enhance the data according to their requirements with the help of Watson Data Refinery on IBM Cloud Pak for Data and Watson Studio on IBM Cloud.

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Watson Studio

Simplify and scale data science to predict and optimize your business outcomes

Tutorials

Streamlining data and Machine Learning workflows with Watsonx Studio Pipelines

Use Watson Pipelines to build model pipelines

Generate machine learning model pipelines to choose the best model for your problem

Serve a custom model on a Kubernetes or Red Hat OpenShift cluster

Orchestrating end-to-end machine learning workflows with Watson Pipelines

Build a recurrent neural network using Pytorch

Implement autoencoders using TensorFlow

Automate model building with AutoAI

Predict future product demand using SPSS Modeler

Build a recurrent neural networks using TensorFlow Keras

Optimize plant selection based on cost and capacity with Decision Optimization

Collect, cleanse, and enhance your data