Adding comments and annotations to SPSS Modeler flows
If you need to document your flows, you can attach explanatory comments to nodes and
model nuggets. These comments can help describe the flow to others in your organization.
Others can then view these comments on-screen, or they can print an image of the flow that
includes the comments. You can also use the Annotations tab in the properties of a node to add notes
to nodes and model nuggets. These annotations are visible only when the Annotations tab is open.
Comments
Comments are text boxes in which you can enter any amount of text, and you can add as many
comments as you like. A comment can be freestanding (not attached to any flow objects), or it can be
connected to one or more nodes or model nuggets in your flow. Freestanding comments are typically
used to describe the overall purpose of the flow. Connected comments describe the specific node or
nugget to which they are attached. Nodes and nuggets can have more than one comment attached, and
the flow can have any number of freestanding comments.
To create a comment:
Right-click your flow canvas and select the overflow menu , then select New comment.
Move the comment to where you want it on the canvas.
Double-click the comment to add text.
The appearance of the text box changes to indicate the current mode of the comment, as the
following table shows.
Table 1. Comment and annotation text
box modes
Comment text box
Mode
Indicates
Obtained by...
Edit
Comment is open for editing.
Creating a new comment or double-clicking an existing one.
Last selected
Comment can be moved, resized, or deleted.
Clicking the flow canvas background after editing, or single-clicking an existing
comment.
View
Editing is complete.
Clicking another node or comment after editing.
Tips:
If you select one or more nodes first and then add a comment, the comment connects to the nodes
automatically.
To connect a comment to nodes or model nuggets, drag from the small circle of the comment
box.
To delete a connection from a node to a comment, hover over the dashed connection line,
right-click, and select Delete.
Annotations
Nodes and model nuggets can be annotated in several ways. You can add descriptive annotations and
specify a custom name for the node. Annotations are stored in the node properties. Annotations are
useful if you want to help distinguish between similar nodes on the flow canvas, but you don't want
comment text boxes visible on the canvas itself.
To create an annotation:
Double-click the node for which you want to add an annotation. The node properties pane
opens.
If you want to give the node a custom name, select Custom name enter the
name. This name shows up as a hover tooltip for the node on the canvas.
In the Annotation field, type the annotation text.
Figure 1. Annotation
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
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You'll learn what you need, how to get it, and where to use it.
Tasks you'll do
Some tasks have a choice of tools and services.
Tools you'll use
Some tools perform the same tasks but have different features and levels of automation.
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Automate the model lifecycle, including preparing data, training models, and creating deployments.
Work with R notebooks and scripts in an integrated development environment.
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deploy and run your data science and AI solutions in a test or production environment.
Find and share your data and other assets.
Import asset metadata from a connection into a project or a catalog.
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Measure and monitor the quality of your data.
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Create your business vocabulary to enrich assets and rules to protect data.
Track data movement and usage for transparency and determining data accuracy.
Track AI models from request to production.
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Create a virtual table to segment or combine data from one or more tables.
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
Discover, profile, catalog, and share trusted data in your organization.
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
View, access, manipulate, and analyze your data without moving it.
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Where you work with data.
> Projects > View all projects
Where you find and share assets.
> Catalogs > View all catalogs
Where you deploy and run assets that are ready for testing or production.
> Deployments
Where you manage governance artifacts.
> Governance > Categories
Where you virtualize data.
> Data > Data virtualization
Where you consolidate data into a 360 degree view.
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