Choosing a tool in Watson Studio

Watson Studio provides a range of tools for users with all levels of experience in preparing, analyzing, and modeling data, from beginner to expert.

To pick the right tool, consider these factors.

The type of data you have
  • Tabular data in delimited files or relational data in remote data sources
  • Image files
  • Textual data in documents
The type of tasks you need to do
  • Prepare data: cleanse, shape, visualize, organize, and validate data.
  • Analyze data: identify patterns and relationships in data, and display insights.
  • Build models: build, train, test, and deploy models to classify data, make predictions, or optimize decisions.
How much automation you want
  • Code editor tools: Use the Jupyter notebook editor or the RStudio IDE to write code to work with any type of data and do any type of task.
  • Graphical canvas tools: Use menus and drag-and-drop to visually program. Build dashboards to analyze data or build multi-step flows to prepare data, analyze data, or build models.
  • Automatic builder tools: Use to build and train models with very limited user input.

Find the right tool:

Tools for tabular or relational data

Tools for tabular or relational data by task:

Tool Tool type Prepare data Analyze data Build models
Jupyter notebook editor Code editor
RStudio Code editor
Data Refinery Graphical canvas  
Streams flow editor Graphical canvas  
Dashboard editor Graphical canvas    
SPSS Modeler Graphical canvas
Spark MLlib modeler Graphical canvas  
Decision Optimization model builder Graphical canvas and code editor  
AutoAI Automatic builder  

Tools for textual data

Tools for building a model that classifies textual data:

Tool Code editor Graphical canvas Automatic builder
Jupyter notebook editor    
RStudio    
SPSS Modeler    
Neural network modeler    
Experiment builder    
Synthesized Neural Network tool    
Natural Language Classifier modeler    

Tools for image data

Tools for building a model that classifies images:

Tool Code editor Graphical canvas Automatic builder
Jupyter notebook editor    
RStudio    
Neural network modeler    
Experiment builder    
Synthesized Neural Network tool    
Visual Recognition modeler    

Accessing tools

To use a tool, you must create an asset specific to that tool, or open an existing asset for that tool.

To create an asset, click Add to project and then choose the asset type you want.

This table shows the asset type to choose for each tool.

To use this tool Choose this asset type
Jupyter notebook editor Jupyter notebook
Data Refinery Data Refinery flow
Streams flow editor Streams flow
Dashboard editor Dashboard
SPSS Modeler Modeler flow
Spark MLlib modeler Modeler flow
Decision Optimization model builder Decision Optimization
Neural network modeler Modeler flow
AutoAI AutoAI experiment
Synthesized Neural Network tool Synthesized neural network
Experiment builder Experiment
Visual Recognition modeler Visual Recognition model
Natural Language Classifier modeler Natural Language Classifier model

To edit notebooks with RStudio, click Launch IDE > RStudio.

Jupyter notebook editor

Use the Jupyter notebook editor to create a notebook in which you run code to prepare, visualize, and analyze data, or build and train a model.

Data format
Any
Data size
Any
How you can prepare data, analyze data, or build models
Write code in Python, R, or Scala
Include rich text and media with your code
Work with any kind of data in any way you want
Use preinstalled or install other open source and IBM libraries and packages
Schedule runs of your code
Import a notebook from a file, a URL, or the Gallery
Share read-only copies of your notebook externally
Get started
To create a notebook, click Add to project > Notebook.
Learn more
Load and analyze public data sets video
Videos about notebooks
Sample notebooks
Documentation about notebooks

Data Refinery

Use Data Refinery to prepare and visualize tabular data with a graphical flow editor. You create and then run a Data Refinery flow as a set of ordered operations on data.

Data format
Tabular: Avro, CSV, JSON, Parquet, or plain text files
Relational: Tables in relational data sources
Data size
Any
How you can prepare data
Cleanse, shape, organize data with over 60 operations
Save refined data as a new data set or update the original data
Annotate data with crowd annotation platforms
Profile data to validate it
Write R scripts to manipulate data
Schedule recurring operations on data
How you can analyze data
Visualize data with over 40 types of graphs
Get started
To create a Data Refinery flow, click Add to project > Data Refinery flow.
Learn more
Videos about Data Refinery
Shape Data video
Documentation about Data Refinery

Streams flow editor

Use the streams flow editor to access and analyze streaming data. You can create a streams flow with a wizard or with a flow editor on a graphical canvas.

Required service
Streaming Analytics
Data format
Streaming data as JSON messages
Streaming binary data
Data size
Any
How you can prepare data
Ingest streaming data
Aggregate, filter, and process streaming data
Process streaming data for a model
How you can analyze data
Run real-time analytics on streaming data
Get started
To create a streams flow, click Add to project > Streams flow.
Learn more
Streams flow Overview video
Videos about streams flows
Documentation about streams flows

Dashboard editor

Use the Dashboard editor to create a set of visualizations of analytical results on a graphical canvas.

Required service
Cognos Dashboard Embedded
Data format
Tabular: CSV files
Relational: Tables in some relational data sources
Data size
Any size
How you can analyze data
Create graphs without coding
Include text, media, web pages, images, and shapes in your dashboard
Share interactive dashboards externally
Get started
To create a dashboard, click Add to project > Dashboard.
Learn more
Dashboards for Interactive and Informative Data Visualizations
Videos about dashboards
Documentation about dashboards

SPSS Modeler

Use SPSS Modeler to create a flow to prepare data and build and train a model with a flow editor on a graphical canvas.

Data formats
Relational: Tables in relational data sources
Tabular: Excel files (.xls or .xlsx) or CSV files
Textual: In the supported relational tables or files
Data size
Any
How you can prepare data
Use automatic data preparation functions
Write SQL statements to manipulate data
Cleanse, shape, sample, sort, and derive data
How you can analyze data
Visualize data with over 40 graphs
Identify the natural language of a text field
How you can build models
Build predictive models
Choose from over 40 modeling algorithms
Use automatic modeling functions
Model time series or geospatial data
Classify textual data
Identify relationships between the concepts in textual data
Get started
To create an SPSS Modeler flow, click Add to project > Modeler flow and then choose IBM SPSS Modeler.
Learn more
SPSS Modeler - refreshed UI for an enterprise data science powerhouse video
Documentation about SPSS Modeler

Spark MLlib modeler

Use the SparkML modeler to create a flow to prepare relational data and build and train a model with a flow editor on a graphical canvas.

Required service
Watson Machine Learning
Data format
Accepts data organized into named columns, such as a Spark DataFrame. The columns can store text, feature vectors, true labels, and predictions.
Data size
Any
How you can prepare data
Transform data with SQL statements
How you can build models
Build predictive or classification models
Choose from 10 Spark MLlib modeling algorithms
Get started
To create a Spark MLlib modeler flow, click Add to project > Modeler flow and then choose Spark.
Learn more
Documentation about Spark MLlib modeler

Decision Optimization model builder

Use Decision Optimization to build and run optimization models in the Decision Optimization modeler or in a Jupyter notebook.

Data formats
Tabular: CSV files
Data size
Any
How you can prepare data
Import relevant data into a scenario and edit it.
How you can build models
Build prescriptive decision optimization models.
Create, import and edit models in Python DOcplex, OPL or with natural language expressions.
Create, import and edit models in notebooks.
How you can solve models
Run and solve decision optimization models using CPLEX engines.
Investigate and compare solutions for multiple scenarios.
Create tables, charts and notes to visualize data and solutions for one or more scenarios.
Get started
To create a Decision Optimization model, click Add to project > Decision Optimization, or for notebooks click Add to project > Notebook.
Learn more
Introduction to Decision Optimization for Watson Studio
Decision Optimization videos
Documentation about Decision Optimization

Neural network modeler

Use the Neural Network Modeler to design a neural network for text and image data with a flow editor on a graphical canvas.

Data format
Textual: CSV files with labeled text data
Image: Image files in a PKL file. For example, a model testing signatures uses images resized to 32×32 pixels and stored as numpy arrays in a pickled format.
Data size
Extremely large data sets
How you can build models
Create a deep learning flow to design and run experiments without coding
Tune many hyperparameters
Standardize the components of a deep learning experiment for easier collaboration
Get started
To create a neural network model, click Add to project > Modeler flow, then select Neural Network Modeler as the flow type.
Learn more
Neural Network Modeler and Deep Learning Experiments on Watson Studio
Videos about deep learning
Documentation about Neural Network modeler

AutoAI tool

Use the AutoAI tool to automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.

Required service
Watson Machine Learning
Data format
Tabular: CSV files
Data size
Less than 100 MB
How you can prepare data
Automatically transform data, such as impute missing values
How you can build models
Train a binary classification, multiclass classification, or regression model
View a tree infographic that shows the sequences of AutoAI training stages
Generate a leaderboard of model pipelines ranked by cross-validation scores
Save a pipeline as a model
Get started
To create an AutoAI experiment, click Add to project > AutoAI experiment.
Learn more
Documentation about AutoAI

Synthesized Neural Network tool

Use the Synthesized Neural Network tool to fully automate the synthesis and training of a neural network with your image or text training data.

Required service
Watson OpenScale
Data format
Textual: CSV files with labeled textual data (UTF-8 encoded and English-only)
Image: Image files in a compressed file plus a CSV file that labels the image files
Data size
Extremely large data sets
How you can build models
Create a deep learning flow to design and run experiments
Use built-in training data
Automatically test a series of algorithm and optimization options
Track, audit, and tune the model in production on a Watson OpenScale dashboard
Get started
To create a model with the synthesized neural network tool, click Add to project > Synthesized neural network.
Learn more
Documentation about Synthesized Neural Networks

Experiment builder

Use the Experiment builder to build deep learning experiments and run hundreds of training runs. This method requires that you provide code to define the training run. You run, track, store, and compare the results in the Experiment Builder graphical interface, then save the best configuration as a model.

Data format
Textual: CSV files with labeled textual data
Image: Image files in a PKL file. For example, a model testing signatures uses images resized to 32×32 pixels and stored as numpy arrays in a pickled format.
Data size
Large data sets
How you can build models
Write Python code to specify metrics for training runs
Write a training definition in Python code
Define hyperparameters, or choose the RBFOpt method or random hyperparameter settings
Find the optimal values for large numbers of hyperparameters by running hundreds or thousands of training runs
Run distributed training with GPUs and specialized, powerful hardware and infrastructure
Compare the performance of training runs
Save a training run as a model
Get started
To create an experiment, click Add to project > Experiment.
Learn more
Neural Network Modeler and Deep Learning Experiments on Watson Studio video
Documentation about Experiment builder

Visual Recognition modeler

Use the Visual Recognition modeler to automatically train a model to classify images for scenes, objects, and other content.

Required service
Visual Recognition
Data format
Image: JPEG or PNG files in a .zip file, separated by class
Data size
Small to medium data sets
How you can build models
Collaborate to classify images
Use a built-in models
Test the model with sample images
Use CoreML to develop iOS apps
Provide as few as 10 images per class
Add or remove images to retrain the model
Use Watson Visual Recognition APIs in applications
Get started
To create a Visual Recognition model, click Add to project > Visual Recognition model.
Learn more
Get Started With Visual Recognition video
Videos about Visual Recognition
Documentation about Visual Recognition

Natural Language Classifier modeler

Use the Natural Language Classifier modeler to automatically train a model to classify text according to classes you define.

Required service
Natural Language Classifier
Data format
Textual: CSV files with sample text and class names
Data size
Small to medium data sets
How you can build models
Provide as few as 3 text samples per class
Collaborate to classify text samples
Test the model with sample text
Add or remove test data to retrain the model
Classify text in eight languages other than English
Use Watson Natural Language Classifier APIs in applications
Get started
To create a Natural Language Classifier model, click Add to project > Natural Language Classifier model.
Learn more
Documentation about Natural Language Classifier modeler

RStudio IDE

Use RStudio IDE to analyze data or create Shiny applications by writing R code.

Data format
Tabular or relational data
Textual data
Images
Unstructured data
Data size
Any size
How you can prepare data, analyze data, and build models
Write code in R
Create Shiny apps
Use open source libraries and packages
Include rich text and media with your code
Prepare data
Visualize data
Discover insights from data
Build and train a model using open source libraries
Get started
To use RStudio, click Launch IDE > RStudio.
Learn more
Overview of RStudio IDE video
Videos about RStudio
Documentation about RStudio