Environments are how you configure compute resources for running assets in tools.
Jobs are how you manage and schedule the running of assets in tools.
Project documentation and notifications are how you stay informed about what's happening in the project.
Asset storage is where project information and files are stored.
Integrations are how you incorporate external tools.
Services are how you add tools or processing power to your project.
Catalogs are how you share assets between projects.
You can customize projects to suit your goals. You can change the contents of your project and almost all of its properties at any time. However, you must make these choices when you create the project because you can't change them later:
Whether to restrict eligible collaborators to your company's employees, or members of your IBM Cloud account.
Whether to enable catalog access by restricting collaborator eligibility.
The instance of IBM Cloud Object Storage to use for project storage.
You can view projects that you create and collaborate in by selecting Projects > View all projects in the navigation menu, or by viewing the Projects pane on the main page.
Collaboration in projects
As a project creator, you can add other collaborators and assign them roles that control which actions they can take. You automatically have the Admin role in the project, and if you give other collaborators the Admin role, they can add collaborators too. See Adding collaborators and Project collaborator roles.
Tip: If appropriate, add at least one other user as a project administrator to ensure that someone is able to manage the project if you are unavailable.
Collaborator eligibility
When you create a project, you can control who is eligible to be added as collaborators:
Restrict who is eligible to be added as a collaborator to people who are internal to your organization by selecting the Restrict who can be a collaborator checkbox. When you select this option, you can add only members of
your IBM Cloud account, or, if your company has SAML federation set up in IBM Cloud, employees of your company. This option also allows access to catalog assets from the project. If you have IBM Knowledge Catalog, this option is selected
by default.
Allow anyone to be added as a collaborator. If necessary, clear the checkbox.
This setting is permanent. You can't change it after you create the project.
Collaboration on assets
Assets are locked during editing to prevent conflicts between changes made by different collaborators. All collaborators work with the same copy of each asset. Only one collaborator can edit an asset at a time. While a collaborator is editing
an asset in a tool, that asset is locked. Other collaborators can view a locked asset, but not edit it. See Managing assets.
Data assets
You can add these types of data assets to projects:
Data assets from local files, catalogs, or the Resource hub
Connections to cloud and on-premises data sources
Connected data assets from an existing connection asset that provide read-only access to a table or file in an external data source
Imported data assets from an existing connection asset that provide read-only access to a table or a file in an external data source
Folder data assets to view the files within a folder in a file system
When you run a tool, you create an asset that contains the information for a specific goal. For example, when you run the Data Refinery tool, you create a Data Refinery flow asset that defines the set of ordered operations to run on a specific
data asset. Each tool has one or more types of associated assets that run in the tool. Some types of assets can run in more than one tool, for example, notebook assets. Assets that run in tools are also known as operational assets.
The tools that you can use in a project depend on the services that you have.
To see which tools you use in a project and which services those tools require, open the tools and services map.
Environments
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Environments control your compute resources. An environment template specifies hardware and software resources to instantiate the environment runtimes that run your assets in tools.
Some tools have an automatically selected environment template. However, for other tools, you can choose between multiple environments. When you create an asset in a tool, you assign an environment to it. You can change the environment for an
asset when you run it.
watsonx.ai Studio includes a set of default environment templates that vary by coding language, tool, and compute engine type. You can also create custom environment templates or add services that provide environment templates. For example,
you can associate the IBM Analytics Engine service to your project to provide extra compute power.
The compute resources that you consume in a project are tracked. Depending on your offering plan, you have a limit to your monthly compute resources or you pay for all compute resources.
A job is a single run of an asset in a tool with a specified environment runtime. You can schedule one or repeating jobs, monitor, edit, stop, or cancel jobs. See Jobs.
Asset storage
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Each project has a dedicated, secure storage bucket that contains:
Files that you upload to the project as data assets.
Data assets from files that you copy from another workspace.
Files that you save to the project with a tool.
Files for assets that run in tools, such as notebooks.
Saved models.
The project readme file and internal project files.
When you create a project, you must select an instance of IBM Cloud Object Storage or create a new instance. You cannot change the IBM Cloud Object Storage instance after you create the workspace. See Object storage.
When you delete a project, its storage bucket is also deleted.
Additional services
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You can associate more services with a project to add tools, compute environments, or other functionality. See Adding associated services.
Integrations with external tools
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Integrations provide a method to interact with tools that are external to the project.
While you create a project, you can add a short description to document the purpose or goal of the project. You can edit the description later, on the project's Settings page.
You can mark the project as sensitive. When users open a project that is marked as sensitive, a notification is displayed stating that no data assets can be downloaded or exported from the project.
The Overview page of a project contains a readme file where you can document the status or results of the project. The readme file uses standard Markdown formatting. Collaborators
with the Admin or Editor role can edit the readme file.
You can view recent asset activity in the Assets pane on the Overview page, and filter the assets by selecting By you or By all using the dropdown. By you lists
assets that you edited, ordered by most recent. By all lists assets that are edited by others and also by you, ordered by most recent.
All collaborators in a project are notified when a collaborator changes an asset.
Catalog integration
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A catalog is a central repository for assets where you can easily find and share data and other assets. Before you can access a catalog, a catalog administrator must add you as a catalog collaborator.
A catalog has the same type of roles as a project. With any catalog role, you can copy assets from the catalog into a project to use them. With the Editor or Admin role in the catalog, you can create assets in a project and then publish them into the catalog.
If you want to access catalogs in a project, you must select the Restrict who can be a collaborator option when you create the project. This setting keeps the company data in your catalog secure. You can't enable catalog integration
in a project after creation. See Collaborator eligibility.
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.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
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.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
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.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
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.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.