What’s new (2020 - now)

Check out the new features for Cloud Pak for Data as a Service, the core services of Watson Studio, Watson Machine Learning, and Watson Knowledge Catalog, and other services each week for 2021 and 2020. Go to entries for 2020.

Week of 15 September 2021

CPLEX and CPO models support (Watson Studio)

You can now import and solve CPLEX and CPO files in Decision Optimization Experiments. See Build model view.

Python 3.8 support in Decision Optimization experiments (Watson Studio)

By default, Decision Optimization experiments use Python 3.7. However, you can edit the run parameters for your experiment to use Python 3.8 instead.

Week ending 10 September 2021

New sample for Watson Studio Pipelines

Download a pre-populated sample project from the Watson Studio Gallery to test the capabilities of Watson Studio Pipelines. Follow the instructions in the Overview page of the sample project to set up the assets you will need to configure and run the flow.

Week of 6 September 2021

Metadata enrichment at scale (Watson Knowledge Catalog)

A new automated tool for use in analytics projects lets you automate data stewardship. Enrich your data at scale by profiling and analyzing data quality of large amounts of data with one click. Learn more about metadata enrichment.

This tool is available only for customers with Watson Knowledge Catalog Professional or Enterprise plans.

Week ending 3 September 2021

New nodes for Watson Studio Pipelines beta

The Watson Studio Pipelines beta has three new nodes. You can now create a pipeline that invokes a DataStage job with the Run DataStage flow node. You can control pipeline execution order with the new Wait for all results node, that specifies to wait for all upstream dependencies to complete, and the Wait for any result node, that specifies to wait for only the first upstream dependency to complete. For details on configuring these nodes, see Configuring pipeline components.

Data Replication restricted beta

IBM Data Replication on Cloud, which provides trusted data integration and synchronization to help you efficiently share data, is now in a restricted beta. It powers the use of real-time information for DataOps by enriching big data, data warehouses, and analytics systems with the most up-to-date data from constantly changing transaction processing databases.

This IBM solution supports delivery of high volumes of data with very low latency, making it ideal for multisite data distribution, and data consolidation, whether across the data center, from on premises to the cloud, or from one cloud to another. The planned robust support for sources, targets, and platforms ensures that the right data is available in data lakes, data warehouses, data marts, and point-of-impact solutions, while enabling optimal resource utilization and rapid ROI.

To participate in the restricted beta and view product documentation, you must register. For more information, see Register Today for the Beta of IBM Data Replication on Cloud

IBM DataStage updates

• The first delivery of bulk editing column metadata is now available.
• When you import a DataStage archive (ISX) to a project, you can now download a CSV report.

Week ending 27 August 2021

Announcing Watson Studio Pipelines beta

Watson Studio Pipelines provides a graphical interface for orchestrating an end-to-end pipeline of assets from creation through deployment. Use the Pipelines editor to automate an end-to-end flow to prepare data, then create, train, deploy, and update machine learning models and Python scripts. Explore the tool’s capabilities using a pre-installed sample pipeline. For details, see Watson Studio Pipelines.

Note: This tool is provided as a beta release and is not supported for use in production environments.

Removal of data annotation with Defined Crowd and Figure Eight

Starting on September 16, you will no longer be able to use the third-party crowd annotation platforms of Defined Crowd or Figure Eight to create annotation jobs.

New data sources supported for Metadata Import (Watson Knowledge Catalog)

Databases for MongoDB and MongoDB are now supported data sources for Metadata Import in projects.

New SPSS Modeler documentation (Watson Studio)

A new Reference information section has been added, covering topics such as tips and shortcuts, a CLEM language reference, and SPSS statistical algorithms. Additional information will be added to this section in the future, such as a scripting and automation guide.

Federated Learning (Watson Machine Learning) enhancements

Federated Learning now supports Python 3.8 as well as 3.7 for model version. Parties can now choose between the versions for their models but all parties and the aggregator must use the same version of Python.

More industry accelerators for end-to-end solutions (Watson Studio)

Two new industry accelerators are available as predefined assets you can use to address common business challenges:

IBM DataStage updates

• ODBC Connector is now supported.
• Transformer expression builder now supports macros. Added feature to find the data type for an expression that is created within the expression builder for stage and loop variables. Delivered usability improvements for the Transformer Expression Builder and the “add column” function.
• National Language Support (NLS) per column mapping and locale section is now supported.
• Added improvements to the log dot feature.
  
  

Week ending 13 August 2021

More industry accelerators for end-to-end solutions (Watson Studio)

Two new industry accelerators are available as predefined assets you can use to address common business challenges:

IBM DataStage updates

• Enhancement of the Transformer expression builder where the column data type is automatically chosen based on the return of the expression.
• Enhancement to View Log button with colored dots denoting warning, failure, or successful run.
  
  

Week ending 06 August 2021

Add a serving name for an online deployment (Watson Machine Learning)

Specify a custom serving name for the URL for an online deployment. See Creating an online deployment.

Support for Python 3.8 (Watson Studio) and (Watson Machine Learning)

You can now select Python 3.8 environments when working with notebooks with and without GPU in Watson Studio. Only environments that include the CPLEX and the DOcplex libraries are currently not available with Python 3.8. See Notebook environments. You can also deploy assets using Python 3.8 frameworks and software specifications. See Supported frameworks and Software specifications for details.

Reminder: Deprecation of Natural Language Classifier

IBM will begin sunsetting IBM Watson Natural Language Classifier (NLC) on August 9, 2021. From September 9, 2021 onward, you will no longer be able to create any new NLC instances. All existing instances will be supported for a period of one year from this announcement. The service will no longer be available from 8 August 2022. Consider migrating to IBM Watson Natural Language Understanding (NLU), a service available on the IBM Cloud. NLU uses deep learning to extract data and insights from text such as entities, keywords, sentiment, and emotion to provide insights for your business and industry.

IBM DataStage updates

• You can now use the NLSMap section at the stage level.
• The log panel can now be resized by using a click and move mechanism.
  
  

Week ending 30 July 2021

Getting started tutorials

You can now view getting started documentation based on the type of task you want to perform:

• Prepare data: Cleanse, shape, integrate, or transform your data.
• Analyze and visualize data: Find insights from your data.
• Build and deploy a model: Solve a business problem.
• Curate and govern data: Enrich and protect your data.

Each getting started path includes one or more tutorials and links to additional resources.

Support for more secure access to remote data (Watson Machine Learning)

Action required Starting July 28 2021, Watson Machine Learning will deprecate support for inline credentials to leverage the latest security best practices and to standardize and simplify data access.

Previously, you could include credentials to directly access a data asset, such as content from Cloud Object Storage or a Db2 table, when you specified the data source for a deployment job or Decision Optimization solution in Watson Machine Learning. To simplify the process for connecting to remote data and to avoid exposing credentials, directly connecting to a data asset will no longer be supported for an inline data source by September 29, 2021. Instead, create a connection to the data asset to securely store the credentials, and then access the data asset using connection attributes. For example:

"input_data_references": [{
                    "type": "connection_asset",
                    "connection": {
                            "id": <connection_guid>
                    },
                    "location": {
                            "bucket": <bucket name>,
                            "file_name": <directory_name>/<file name>
                            <other wdp-properties supported by runtimes>
                    }
            }]

Notice in the sample how the “type” of the input data reference is “connection_asset.”

Starting on September 29, inline credentials will fail with an error of invalid fields. If you are using Cloud Object Storage (S3) or Db2 to provide inputs or store the results of your Decision Optimization jobs in Watson Machine Learning you will need to take action by September 29, 2021. This change applies to the Watson Machine Learning v4 APIs (/ml/v4) as well as to the Watson Machine Learning v4 Python client library.

See Creating connections, and Batch deployment details. For details specific to updating Decision Optimization models, see this blog post on using connection assets with Decision Optimization.

Export and import of all governance artifacts from a single file (Watson Knowledge Catalog)

You can now export all governance artifacts to a single ZIP file and import them all at once by using REST API. See Importing all governance artifacts from a ZIP file and Exporting all governance artifacts to a ZIP file.

Enhancements in Federated Learning (Watson Machine Learning)

IBM Federated Learning now includes Pytorch 1.7.1. A new hyperparameter for Probabilistic Federated Neural Matching (PFNM) is available for neural network models with heterogenous data sets.

IBM DataStage updates

• Support for ‘cut’ of (partial flows and whole flows) is now added.
• Transformer expression builder has been enhanced such that a single click on the left side panel shows information about the function, and double-clicking a function adds it to the expression.
• Sybase ASE Connector is now available in the palette.
• Pagination is now available for ISX imports.
  
  

Week ending 23rd July 2021

IBM DataStage updates

• Runtime parameters can now be used in the Transformer stage expression builder.
• Support for import and export of data definitions is available for the Transformer stage.

Number of Spark executors restricted for Watson Studio Lite plan

Watson Studio Lite plan users are able to use only 2 executors for Spark environments in all regions. Paid plan (Standard and Enterprise) users can use the maximum number of executors that are available on the Spark cluster.

Week ending 16 July 2021

Updated Watson Studio notebook environments and Watson Machine Learning deployment frameworks

Action required Starting on July 16, 2021, new Python environments for notebooks and new deployment frameworks are available to support latest functionality and security best practices. Your action is required to update affected notebooks or retrain AutoAI experiments by October 15, 2021. Deployments might also need updating as older frameworks or software specifications are deprecated, then removed.

Changes to Python notebook environments

Deprecated environments for notebooks are marked as deprecated. For example, (Deprecated) Default GPU Python 3.7 or (Deprecated) Default Python 3.7. Custom environment definitions based on deprecated environments will also be marked as deprecated. You will be unable to create a new notebook using a deprecated environment and software configuration starting on August 19, 2021, and existing notebooks will stop running on October 15, 2021 unless you update them to a supported configuration.

Changes to Deployment frameworks and software specifications

In addition to support for updated deployment framework versions, the default_py3.7 python software specification used with these frameworks and other deployed assets will be deprecated in favor of the new default_py3.7_opence software specification. 

If you have a deployed asset, such as a model, corresponding to a deployment framework listed as deprecated, update the deployment to the latest framework by October 15, 2021 to continue service uninterrupted. For AutoAI models, retrain the experiment to update the asset to the newer software specification, then redeploy the resulting model.

All other Supported frameworks versions remain the same.

When to take action

Take the following actions by October 15, 2021:

1. If you have any custom notebook environment definitions based on deprecated environments, create new definitions based on a supported software configuration. For example, Default Python 3.7 GPU or Default Python 3.7. 
2. Change the environments of notebooks to use a supported environment.
3. Test your notebooks. If a notebook with a new environment does not run successfully, check the TensorFlow version compatibility guide or the PyTorch release note on backward incompatible changes to see if you need to make any modifications for a smooth transition. In many cases, TensorFlow and PyTorch is backward compatible.
4. Stop any existing jobs that run notebooks with a deprecated Python 3.7 CPU or GPU environment.
5. Create new jobs that use the Default GPU Python 3.7 or Default Python 3.7 environment or your new custom environments.
6. Retrain AutoAI experiments to automatically use the new software specification.
7. Redeploy any models in Watson Machine Learning using supported frameworks and software specifications.

Learn more about working with Watson Studio GPU Notebooks and deploying with Watson Machine Learning:

• Notebook environments in Watson Studio
• GPU notebook environments in Watson Studio
• Schedule Notebook jobs in Watson Studio
• Supported frameworks in Watson Machine Learning

Action required Support for more secure access to remote data (Watson Machine Learning)

Previously, you could include credentials to directly access a data asset, such as content from Cloud Object Storage or a DB2 table, when you specified the data source for a deployment job or Decision Optimization solution in Watson Machine Learning. To simplify the process for connecting to remote data and to avoid exposing credentials, directly connecting to a data asset will no longer be supported for an inline data source. Instead, create a connection to the data asset to securely store the credentials, and then access the data asset using connection attributes. For example:

"input_data_references": [{
                    "type": "connection_asset",
                    "connection": {
                            "id": <connection_guid>
                    },
                    "location": {
                            "bucket": <bucket name>,
                            "file_name": <directory_name>/<file name>
                            <other wdp-properties supported by runtimes>
                    }
            }]

Starting on September 29, inline credentials will fail with an error of invalid fields. This change applies to all /ml/v4 API and Watson Machine Learning Python client calls that contain data references.

See Creating connections and Batch deployment details for usage details.

IBM DataStage updates

• Support for runtime environment variables was added.

Week ending 9 July 2021

IBM DataStage updates

• IBM DataStage supports the Hybrid Subscription Advantage program to offer generous discounts on the DataStage Standard Plan cloud service. To learn more, see Activating the Hybrid Subscription Advantage.
• You can preview data for Sequential File source nodes.
• The Transformer stage was updated to support the following features:
  • Start the derivation builder from the Transformer output’s constraint field.
  • Complete bulk actions for Transformer output columns.
  • Search for functions in the Transformer derivation builder.
  • Choose local parameters in the Transformer derivation builder.
• You can add a shared container (“subflow”) from the asset browser.
• The Modify stage has tear sheet support to specify how the Modify stage operates.
• You can edit column metadata type subproperties for the Row Generator stage.
• The canvas default for warnings in run settings is changed to 100.
• The link details card has different icons that indicate the link type.
• The tear sheets have UI updates.

Week ending 2 July 2021

Cloud Pak for Data as a Service learning collection

You can now watch videos and complete tutorials to learn how to use Watson Studio, Watson Knowledge Catalog, Watson Machine Learning, Data Refinery, and much more in Cloud Pak for Data as a Service.

See the Cloud Pak for Data as a Service learning collection.

New enhancements in Federated Learning (Watson Machine Learning)

Federated Learning now extends its support of the party threshold metric (quorum) for XGBoost experiments and upgrades its support for Tensorflow 2.4.2.

Week ending 25 June 2021

Data Virtualization on Cloud Pak for Data as a Service is GA

Data Virtualization as a service is now generally available. Data Virtualization integrates multiple data sources across locations without having to copy and replicate data, and turns all this data into one logical data view. Get started by upgrading your IBM Cloud account and provisioning the service. See Provisioning Data Virtualization.

Learn more about Data Virtualization.

Watson Knowledge Catalog Enterprise plan is available

You can now provision a Watson Knowledge Catalog Enterprise plan.

The Enterprise plan has the following features that are not included in other plans:

• Knowledge Accelerators: Add curated glossaries of governance artifacts for your industry. See Knowledge Accelerators.
• Data Privacy: Produce masked copies of data that are protected with advanced masking options. See Data Privacy is GA!.
• 20 users without extra cost

See Watson Knowledge Catalog plans.

Data Privacy is GA! (Watson Knowledge Catalog)

Protect sensitive data with new Data Privacy capability. Data Privacy allows data administrators to produce masked copies of data for data scientists, business analysts, and application testers. The data is protected with data protection rules that apply automatically to all data imported to a catalog.

Data Privacy also introduces advanced masking options for data protection rules, such as enhanced format preservation, one-way hash tokenization, and reversible encryption. The advanced masking options also provide the ability to maintain relationships and to increase the utility of masked data.

See Data Privacy.

Knowledge Accelerators provide curated glossaries for Watson Knowledge Catalog

You can now add Knowledge Accelerators to your governance framework if you have the Watson Knowledge Catalog Enterprise plan.

Knowledge Accelerators help organize data along a common and known business vocabulary, in addition to automatically providing business context and definitions during the onboarding of regulatory and industry data content within Watson Knowledge Catalog. An extensive business vocabulary accelerates making data understandable and elevates your data catalog investment.

See Knowledge Accelerators.

Limits and defaults for retaining deployment jobs (Watson Machine Learning)

Watson Machine Learning now places limits on the number of deployment jobs retained for each single deployment space. Importantly, with this update, none of your information will be lost, but your user experience when running deployment jobs might need to change.

The Watson Machine Learning plan limits for the number of deployment jobs retained for a single space are:

• Lite: 100
• Standard: 1000
• Professional: 3000 (increase by request via support)

If you exceed your limit, you will be unable to create new deployment jobs until you delete existing jobs or upgrade your plan. New automation will help you stay within the plan limits. By default, jobs metadata will be auto-delete after 30 days. You can override this value when creating a job.

Managing metadata retention and deletion programmatically

If you are managing a job programmatically using the Python client or REST API, you can retrieve metadata from the deployment endpoint using the GET method during the 30 days.

To keep the metadata for more or less than 30 days, change the query parameter from the default of retention=30 for the POST method to override the default and preserve the metadata. Note that changing the value to retention=-1 will cancel the auto-delete and preserve the metadata.

To delete a job programmatically, specify the query parameter hard_delete=true for the Watson Machine Learning DELETE method to completely remove the job metadata. For example:

DELETE /ml/v4/deployment_jobs/{JobsID}

Updated GPU environment (Watson Studio)

Action required Starting on June 25, 2021, the GPU environment for notebooks is updated to leverage the latest packages, technologies, and security best practices. You must take action to switch to the new GPU environment by August 1, 2021.

Key libraries changes in this update include:

The new GPU environment is named Default GPU Python 3.7. The previous Default GPU Python 3.7  environment is renamed to (Deprecated) Default GPU Python 3.7. Custom environment definitions based on the previous Default GPU Python 3.7 environment now have a software configuration named (Deprecated) Default GPU Python 3.7. The deprecated GPU environment and software configuration will stop running on August 1, 2021.

When to take action

If you have PyTorch or XGBoost model in your GPU notebook and require the models to be deployed in Watson Machine Learning, do not change your GPU environment until notice that Watson Machine Learning supports the new versions of the libraries.

Otherwise, if you created GPU notebooks, scheduled any GPU notebook jobs, or deployed any models in GPU notebooks, take the following actions by Aug 1, 2021:

1. If you have any custom GPU environment definitions, create new definitions based on the software configuration Default Python 3.7 GPU. 
2. Change the environments of notebooks that use the (Deprecated) Default GPU Python 3.7 environment to the Default Python 3.7 GPU environment.
3. Change the environments of any notebooks that use custom GPU environments to your new custom GPU environments.
4. Test your notebooks. If a notebook with a new GPU environment does not run successfully, check the TensorFlow version compatibility guide or the PyTorch release note on backward incompatible changes to see if you need to make any modifications for a smooth transition. In many cases, TensorFlow and PyTorch is backward compatible.
5. Stop any existing jobs that run notebooks with a GPU environment. 1 . Create new jobs that use the Default GPU Python 3.7 environment or your new custom GPU environments.
6. Redeploy any models in Watson Machine Learning:
   For Tensorflow models, use the Software Specification tensorflow_2.4-py3.7.

Learn more about working with Watson Studio GPU Notebooks and deploying with Watson Machine Learning:

• GPU environments in Watson Studio
• Schedule Notebook jobs in Watson Studio
• Supported frameworks in Watson Machine Learning

New Decision Optimization features

These features are now available in Decision Optimization:

• You can now sort data tables in the Prepare Data view.
• A video on how to Use Decision Optimization Modeling Assistant showing you how to use advanced features of the Modeling Assistant is now available.

New DataStage features

• You can add column derivation directly in the output section of the Transformer stage.
• SAP OData is enabled in the asset browser.
• The Investigate QualityStage operator is enabled in the palette.

Week ending 18 June 2021

IBM DataStage on Cloud Pak for Data as a Service is GA!

DataStage as a service is now generally available. DataStage offers AI-powered data integration that allows you to extract, transfer, and load data across multiple systems anywhere.

Learn more about DataStage.

More industry accelerators for end-to-end solutions (Watson Studio)

Three new industry accelerators are available as predefined assets you can use to address common business challenges:

• Financial Markets Customer Life Event Prediction: Use the Financial Markets Customer Life Event Prediction accelerator to set your clients on the path to financial success with relevant offers at the right time. The accelerator includes business terms, a set of sample data science assets, and a sample dashboard to visualize the results.
• Utilities Demand Response Program Propensity: Which customers should be offered the opportunity to enroll in the Demand Response Program? Use the Utilities Demand Response Program Propensity accelerator to jump-start your analysis. The accelerator includes business terms, a set of sample data science assets, and a sample RStudio dashboard to visualize the results. You can also optionally explore and visualize the data using the Cognos Dashboard Embedded.
• Utilities Payment Risk Prediction: Use the Utilities Payment Risk Prediction accelerator to proactively engage with customers at risk of missing payments. The accelerator includes business terms, a set of sample data science assets, and a sample dashboard to visualize the results

Additional algorithms for AutoAI experiments (Watson Machine Learning)

Snap ML algorithms are now available for training AutoAI experiments. The algorithms are well suited for balancing accuracy with training speed. For details, see AutoAI implementation details.

Week ending 11 June 2021

MariaDB data source supported for Metadata Import (Watson Knowledge Catalog)

MariaDB is now a supported data source for Metadata Import in projects.

Week ending 4 June 2021

More industry accelerators for end-to-end solutions (Watson Studio)

Three new industry accelerators are available as predefined assets you can use to address common business challenges:

• Utilities Customer Micro-Segmentation: Assigns customers to specific groups, based on their lifestyle and behavioral patterns.
• Financial Markets Customer Segmentation: Assigns customers to specific groups, based on their lifestyle and behavioral patterns.
• Insurance Loss Estimation using Remote Sensing: Uses remote sensing flooding data to assist with assessing insurance claims.

New enhancements in Federated Learning (Watson Machine Learning)

Federated Learning provides new ways for you to tune your experiment, including support for a party threshold metric (quorum) for Tensorflow experiments, and support for termination of an experiment when accuracy thresholds are met.

Enhanced connections interface

When you add a connection to a project, the interface includes enhancements that make it quicker to create the connection:

• Use the Provider filter to identify IBM data sources or third-party data sources.
• Use the Compatible services filter to find the connection types that you can use with a specific service.

Alternatively, if you know the name of the connection type that you are looking for, you can enter it in the Find field.

For the steps to add a connection to a project, see Adding connections to projects.

Week ending 28 May 2021

New Decision Optimization runtime and CPLEX version (Watson Machine Learning)

Decision Optimization has new options:

• New Decision Optimization runtime. When you run a model in a Decision Optimization experiment, the new do_20.1 runtime is now used by default. See Build model view.

• CPLEX V.20.1 is now available in Watson Machine Learning. See Model deployment.

Week ending 21 May 2021

New SPSS Modeler tutorials (Watson Studio)

New tutorials are available for SPSS Modeler, based on example projects. For details, see SPSS Modeler tutorials.

New limits and defaults for retaining deployment jobs (Watson Machine Learning)

On June 23, Watson Machine Learning will introduce limits on the number of deployment jobs retained for each single deployment space. Importantly, with this update, none of your information will be lost, but your user experience when running deployment jobs might need to change.

The Watson Machine Learning plan limits for the number of deployment jobs retained for a single space are:

• Lite: 100
• Standard: 1000
• Professional: 3000 (increase by request via support)

If you exceed your limit, you will be unable to create new deployment jobs until you delete existing jobs or upgrade your plan. New automation will help you stay within the plan limits. By default, jobs metadata will be auto-delete after 30 days. You can override this value when creating a job.

Managing metadata retention and deletion programmatically

If you are managing a job programmatically using the Python client or REST API, you can retrieve metadata from the deployment endpoint using the GET method during the 30 days.

To keep the metadata for more or less than 30 days, change the query parameter from the default of retention=30 for the POST method to override the default and preserve the metadata. Note that changing the value to retention=-1 will cancel the auto-delete and preserve the metadata.

To delete a job programmatically, specify the query parameter hard_delete=true for the Watson Machine Learning DELETE method to completely remove the job metadata. For example:

DELETE /ml/v4/deployment_jobs/{JobsID}

Short videos showcase the Data Refinery GUI operations

Have you found yourself using a Data Refinery operation and thinking it would be helpful to see an example of how to use that operation? Well, you’re in luck! The GUI operations topic now has a short video of each operation.

Send us your comments on these videos at the Watson Studio and Machine Learning community page. (Comments require signing in to the community.)

Name changes for the Sybase connections

These connections have new names:

• Sybase is renamed to SAP ASE
• Sybase IQ is renamed to SAP IQ

Your previous settings for the connections remain the same. Only the connection names have changed.

Week ending 7 May 2021

General availability of AutoAI feature engineering on joined relational data (Watson Machine Learning)

AutoAI feature engineering on joined relational data is now GA in all regions. This new feature significantly shortens the time required for feature engineering when joining multiple relational data files into a training data source for an AutoAI experiment. Increased file limits mean you can now join up to 20 data files, with each data file up to 4 GB and a combined maximum of 20 GB.  AutoAI experiments with joined data you created during the beta period do not require any migration but will now consume Capacity Unit Hours (CUH) from your Watson Machine Learning instance. Billing for AutoAI with joined data begins on Monday, 10 May 2021. For details on CUH consumption, see the plans on IBM Cloud. To learn more about this new feature, see Building an experiment with joined data.

Metadata import enhancements (Watson Knowledge Catalog)

Metadata import was enhanced in the following ways:

• COBOL copybooks
  • When you import COBOL copybooks, the relationships between the copybooks and the corresponding virtual tables are imported into the catalog.
  • You can select individual COBOL copybooks for metadata import.
  • The performance of importing COBOL copybook metadata is improved.
• Usability improvements
  • You have more options when setting the data scope.
  • You can create and add tags to the metadata import asset.
  • You can directly edit the configuration from the review section.
  • You can edit a metadata import asset from within the asset.
  • You can see the status of imported data assets.
• Box is now a supported data source.

See Importing metadata.

Week ending 30 April 2021

Solve common business problems using an industry accelerator

Industry accelerators are pre-populated projects with assets you can download and use to solve business problems, such as analyzing customer attrition. You can also use accelerators as working samples of data science techniques. See End-to-end examples: Industry accelerators. You can also search the Gallery using the industry accelerator tag.

Build a sentiment analysis model using AutoAI (Watson Machine Learning)

A new feature in AutoAI can detect text in a data set and transform it to vectors to perform some text analysis. See Creating a text analysis experiment.

Week ending 23 April 2021

Beta release of AutoAI time series experiments (Watson Machine Learning)

Create a time series experiment to predict future activity (such as stock prices or temperatures) over a specified date/time range. For more information on creating time series experiments see this blog post. For details on using the feature, see Creating a time series experiment.

AutoAI experiments with joined data moving to general availability (Watson Machine Learning)

AutoAI experiments that join multiple relational datasets to create training input will be generally available soon, planned for May 7, 2021. No migration is needed for existing AutoAI experiments or models built using this feature. The close of the beta period means that usage starting from the general availability date will incur charges, billed as consumption units per hour (CUH), against the Watson Machine Learning service. Details on Watson Machine Learning plans and rates will be updated and announced concurrent with the general availability of this feature.

New governance artifacts experience in other regions (Watson Knowledge Catalog)

The new governance artifacts experience and other features are now available in the following IBM Cloud service regions:

• Dallas service region on 22 April
• London service region on 21 April
• Frankfurt service region on 20 April

See these what’s new entries from last week:

• New governance artifacts experience
• Catalog enhancements
• Enhanced profiling of structured data
• New Platform assets catalog

Profiling of unstructured data

Profiling of documents with unstructured data is now available in all IBM Cloud service regions (see this what’s new entry).

Week ending 16 April 2021

New governance artifacts experience (Watson Knowledge Catalog)

Starting on April 16, the new governance artifacts experience is available in the Tokyo service region. It will be available in other regions in upcoming weeks.

You can now use these new governance capabilities:

• Assign Watson Knowledge Catalog service roles and permissions in IAM to control which users can perform which actions within the context of Watson Knowledge Catalog.
• Use categories to organize all your governance artifacts and the users who can view and manage those artifacts.
• Create your own classifications.
• Create your own data classes.
• Define more relationships between governance artifacts.
• Create reference data sets to define values for specific types of columns that can be used as data matching criteria for a data class.
• Create governance rules to provide a description of your governance criteria.

If you have an existing Watson Knowledge Catalog service, you have the option to upgrade to the new experience after it is available in your region. However, your existing governance artifacts are permanently deleted. If you want to retain your existing governance artifacts, you can wait until the automatic upgrade of the governance artifact experience, along with automatic artifact migration, is available in an upcoming month.

If you provision the Watson Knowledge Catalog service after April 22, you have the new experience.

See Upgrading to the new UI.

Catalog enhancements (Watson Knowledge Catalog)

Starting on April 16, the catalog enhancements are available in the Tokyo service region. They will be available in other regions next week.

Catalogs are enhanced in the following ways:

• Additional information is shown on the new Overview page for assets, such as, the asset’s path and related assets.
• More activities are shown on the Activities page for assets.
• You can add more relationships between assets. See Adding asset relationships
• You can now add COBOL copybook as an asset in projects and catalogs. See Adding COBOL copybooks.

Enhanced profiling of structured data (Watson Knowledge Catalog)

Starting on April 16, enhanced profiling of relational data is available in the Tokyo service region. It will be available in other regions next week.

Profiling now also generates an overall quality score for the data asset and individual quality scores for any of the columns in the data asset. See Profiling assets.

Improved search across the platform

You can now use the global search bar to search for assets across all the projects, catalogs, and deployment spaces to which you have access. You can also search for governance artifacts across the categories to which you have access.

The search now finds results across more asset properties and governance artifacts. You can now search for exact words or phrases by surrounding search terms with double quotation marks. See Searching across the platform.

New Platform assets catalog

Starting on April 16, the Platform assets catalog is available in the Tokyo service region. It will be available in other regions next week.

You can now create a Platform assets catalog to share connections across your organization. To create or view the Platform assets catalog, choose Data > Platform connections from the main menu. You can add an unlimited number of collaborators and connections to the Platform assets catalog. See Creating the Platform assets catalog.

Deprecation of importing Information Governance Catalog assets (Watson Knowledge Catalog)

You can no longer import Information Governance Catalog assets into Watson Knowledge Catalog by specifying an archive file with the Add to Catalog > Import Assets menu option.

Support for autogenerated notebook for AutoAI experiment (Watson Machine Learning)

Save your AutoAI experiment code as an auto-generated notebook so you can review the experiment code and interact with the experiment programmatically. The notebook is saved as a project asset that you can review and run. For details, see AutoAI notebooks.

The Master Data Management (Beta) service is now known as IBM Match 360 with Watson (Beta)

The beta service formerly known as Master Data Management is now named IBM Match 360 with Watson. For more information about IBM Match 360 with Watson, see Managing master data (Beta).

Certificate file required for “Db2 for i” and “Db2 for z/OS” connections

Action required To continue to use the “Db2 for i” connection or the “Db2 for z/OS” connection, you must obtain a Db2 Connect Unlimited Edition license certificate file for the corresponding Db2 for z/OS subsystem or the Db2 for i server. For download and installation instructions, see Activating the license certificate file for Db2 Connect Unlimited Edition.

SOC1 Type 2 and SOC2 Type 2 certification achievements

The following services in the Cloud Pak for Data as a Service catalog have achieved SOC1 Type 2 and SOC2 Type 2 certification:

• DB2 on Cloud
• Db2 Warehouse on Cloud (Flex)
• Discovery Service
• IBM Analytics Engine
• Natural Language Understanding
• Natural Language Classifier
• Personality Insights
• Speech to Text
• Text to Speech
• Tone Analyzer
• Visual Recognition
• Watson Assistant
• Watson Knowledge Catalog
• Watson Knowledge Studio
• Watson Machine Learning
• Watson OpenScale
• Watson Studio

Week ending 02 April 2021

Personal credentials supported for connections

When you create a connection to a data source, you now have the option to select personal credentials if you want each user to specify their own credentials to access the connection. Previously all connections used shared credentials, which allow all users to use the same credentials to access the connection. Personal credentials are available only if the account owner enables them on the Account page and if the data source supports personal credentials.

Account page includes resource scope and connection credentials settings

The Account page has been expanded to include resource scope settings and connection credentials settings (personal or shared) for Cloud Pak for Data as a Service. As before, you can access your IBM Cloud account settings from this page.

All connection types are supported on all offering plans (Watson Studio and Watson Knowledge Catalog)

Previously certain connections were limited to Watson Studio Standard or Enterprise plans or to Watson Knowledge Catalog Standard or Professional plans. For the list of connections that are newly available, see Service plan changes for Watson Studio and Watson Knowledge Catalog.

Week ending 26 March 2021

Certificate file will be required for “Db2 for i” and “Db2 for z/OS” connections

An upcoming release will change the properties for “Db2 for i” and “Db2 for z/OS” connections. Connections will not work unless you obtain a Db2 Connect Unlimited Edition license certificate file for the corresponding Db2 for z/OS subsystem or the Db2 for i server. To continue using these connections uninterrupted, you must install the file. For download and installation instructions, see Activating the license certificate file for Db2 Connect Unlimited Edition.

Run notebook workloads from Watson Studio on AWS using Satellite locations

A Satellite location for running notebook workloads is now available on AWS us-east-1 Region. The Satellite location is prebuilt by IBM. You configure your Python or R notebooks to access the environment for the prebuilt Satellite location and the notebook code executes on AWS. For data hosted on AWS, a Satellite location saves time and money by executing the code where the data resides. Currently, prebuilt Satellite locations are available only in the Dallas location, and only for customers with Standard and Enterprise plans. For a description of Watson Studio runtimes on prebuilt Satellite locations, see Satellite locations overview. To learn how to run workloads in a Satellite location, see Environments for Satellite locations.

Week ending 19 March 2021

New Spark environment for running Data Refinery flow jobs

You can now select Default Spark 3.0 & R 3.6 when you select an environment for a Data Refinery flow job. The new environment uses the same capacity unit hours (CUHs) as the other Default environments.

DataStage (Beta)

You can now find all the information about new features, known issues, limitations, and other beta information for the DataStage beta in one place. To learn more, see Welcome to the DataStage beta!.

Improved user experience for Hybrid Subscription Advantage

The Hybrid Subscription Advantage user interface has been improved, so subscribing is faster and easier. We have also added a summary dashboard to view your discounted entitlements. The IBM Hybrid Subscription Advantage program is a licensing benefit that applies existing on-premises Cloud Pak for Data software entitlements within the Cloud Pak for Data as a Service portfolio. To learn more, see Activating the Hybrid Subscription Advantage.

Week ending 12 March 2021

GPU environments adjustment in Watson Machine Learning

Action required Starting on March 19, 2021, GPU environments in Watson Machine Learning will be available only through V2 Standard plan and V2 Professional plan. See Service plan changes and deprecations.

Week ending 05 March 2021

Save an AutoAI experiment with joined data as a notebook (Watson Machine Learning)

You can now save an AutoAI experiment with a joined data set as a notebook so that you can review all of the transformations that go into generating the model pipelines. Note that you can save the whole experiment as a notebook but you cannot save an individual pipeline as a notebook. See Saving an AutoAI generated notebook.

Data Refinery flows are supported in deployment spaces (Watson Machine Learning)

You can now promote a Data Refinery flow from a project to a deployment space. Deployment spaces are used to manage a set of related assets in a separate environment from your projects. You can promote Data Refinery flows from multiple projects to a space. You run a job for the Data Refinery flow in the space and then use the shaped output as input for deployment jobs in Watson Machine Learning. For instructions, see Promote a Data Refinery flow to a space in Managing Data Refinery flows.

New way of profiling documents with unstructured data (Watson Knowledge Catalog)

Data assets that contain unstructured data, such as Microsoft Word, PDF, HTML, and plain text documents, were previously profiled by IBM Watson Natural Language Understanding (Dallas service region only). Such profiles showed a document’s semantic features. To align profiling of structured and unstructured data assets and to improve the governance capabilities, IBM Watson Natural Language Understanding is replaced with a new analysis service for unstructured data that can also infer Watson Knowledge Catalog data classes. Unstructured data assets of the supported types are profiled automatically when they are added to a project or a catalog. See Profiles of data assets.

Profiling of unstructured data is currently available only when you provision Watson Knowledge Catalog in the Dallas (US-South) service region on IBM Cloud.

Week ending 5 February 2021

Deprecation and removal of Streams flows (Streaming Analytics)

The streams flows tool is removed from Watson Studio projects. See Service plan changes and deprecations.

Reminder: Removal of Python 3.6 environment (Watson Studio and Watson Machine Learning)

Action required Python 3.6 is being removed from Watson Studio and Watson Machine Learning due to a security vulnerability. See Service plan changes and deprecations.

Reminder: Support for V1 machine learning instances and deprecated APIs ends on April 8, 2021 (Watson Machine Learning)

Action required The migration period for Watson Machine Learning Standard and Professional plan users to migrate assets from V1 machine learning service instances to V2 machine learning service instances ends on April 8, 2021. This is also the end of support for deprecated V3 and V4-beta Watson Machine Learning APIs. See Service plan changes and deprecations.

Deprecated APIs for Watson OpenScale

As of 15 March 2021, Watson OpenScale requires the use of a new API version. See Service plan changes and deprecations.

Week ending 29 January 2021

New pricing plan for Watson OpenScale

Watson OpenScale has a new Standard v2 pricing plan. See Service plan changes and deprecations.

Week ending 22 January 2021

Open beta for building AutoAI experiments with joined data sets (Watson Machine Learning)

You can now build an AutoAI experiment using up to 5 data sets joined by common keys into a single data set. Use the canvas tool to configure how the data is joined before you run the experiment. When you deploy a resulting model, specify input data that matches the schema of your experiment. For details, see Building an AutoAI experiment using joined data.

Week ending 15 January 2021

New organization for building and deploying assets (Watson Machine Learning)

The content for building models has been separated from the content for deploying and managing models, to make it easier for you to find tools and processes. The updated sections are:

• Analyzing data and building models which documents all of the tools you can use to build models and solutions.
• Deploying and managing models which documents frameworks, tools, and processes for deploying, evaluating, and updating assets.

Week ending 18 December 2020

Deprecation and removal of Streams flows (Streaming Analytics)

Streams flows is being deprecated on January 31, 2021. See Service plan changes and deprecations.

Week ending 4 December 2020

IBM Watson Visual Recognition is discontinued in Watson Machine Learning

IBM Watson Visual Recognition is discontinued. See Service plan changes and deprecations.

Week ending 27 November 2020

Open beta for Federated Learning (Watson Machine Learning)

Federated Learning provides the tools for training a model collaboratively, using a federated set of secure, remote data sources. The data sources are never moved or combined, but they each contribute to training and improving the quality of the common model. The high-level steps are:

1. Define the parties for federated learning and create remote training systems
2. Create a common model and configure how to aggregate the data.
3. Train the model with the federated data sources.
4. Deploy and score the resulting model.

For details on setting up remote parties and training a common model, see Federated Learning.

Hybrid Subscription Advantage

The IBM Hybrid Subscription Advantage program is a licensing benefit that applies existing on-premises Cloud Pak for Data software entitlements within the Cloud Pak for Data as a Service portfolio. To learn more, see Activating the Hybrid Subscription Advantage.

Week of 20 November 2020

HIPAA readiness for Watson Knowledge Catalog

Watson Knowledge Catalog meets the required IBM controls that are commensurate with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Security and Privacy Rule requirements. HIPAA readiness applies to only certain plans and regions. For more information, see Keeping your data secure and compliant.

Week of 13 November 2020

Reminder: Upcoming changes to Watson Machine Learning frameworks

All frameworks that are built with Python 3.6 are deprecated in favor of frameworks that are built with Python 3.7. Spark 2.3 frameworks are deprecated in favor of Spark 2.4. Support for Spark 2.3 will be discontinued on December 1, 2020. See Service plan changes and deprecations.

Week of 28 October 2020

Legacy APIs no longer supported for Watson Machine Learning Lite plan users

The migration period for Watson Machine Learning Lite plan users to migrate assets to the V2 machine learning service instance and the V4 Watson Machine Learning APIs is ended. See Service plan changes and deprecations.

IBM Match 360 with Watson service beta

The IBM Match 360 with Watson service is now in beta. This new IBM Match 360 with Watson experience seamlessly consolidates data from the disparate sources that fuel your business to establish a single, trusted, 360-degree view of your customers.

The beta release of IBM Match 360 with Watson includes two user experiences:

• Master data configuration for data engineers to prepare and configure master data. This experience enables you to:
  • Configure the master data system.
  • Refine the generated data model.
  • Upload or connect data assets and sources.
  • Map data into the model.
  • Run the IBM Match 360 with Watson service’s powerful matching capability to create master data entities.
  • Configuring and tuning the matching algorithm to meet your organization’s requirements.
• Master data explorer for business analysts and data stewards to search, view, analyze, and export master data entities.

The IBM Match 360 with Watson service on Cloud Pak for Data as a Service also includes a rich set of APIs that empower your business applications with direct access to trusted data.

For more information about IBM Match 360 with Watson, see Managing master data (Beta).

Week ending 23 October 2020

New way of adding data (Watson Knowledge Catalog)

Create metadata import assets to configure and run the metadata import for a selected set of data assets into a project or a catalog. For details, see Managing metadata imports.

SJIS encoding available in Data Refinery for input and output

SJIS (“Shift JIS” or Shift Japanese Industrial Standards) encoding is an encoding for the Japanese language.

To change the encoding of the input file, open the file in Data Refinery, go to the Data tab, scroll down to the SOURCE FILE information, and then click the “Specify data format” icon .

To change the encoding of the output (target) file, open the Information pane and click the Details tab. Click the Edit button. In the DATA REFINERY FLOW OUTPUT pane, click the Edit icon to change the encoding.

The SJIS  encoding is supported only for CSV and delimited files.

New visualization charts for Data Refinery and SPSS Modeler (Watson Studio)

To access the charts in Data Refinery, click the Visualizations tab and then select the columns to visualize. The chart automatically updates as you refine the data. 

To access the charts in SPSS Modeler, use a Charts node. The Charts node is available under the Graphs section on the node palette. Double-click the Charts node to open the properties pane. Then click Launch Chart Builder to create one or more chart definitions to associate with the node.

For the full list of available charts, see Visualizing your data.

• Evaluation charts are combination charts that measure the quality of a binary classifier. You need three columns for input: actual (target) value, predict value, and confidence (0 or 1). Move the slider in the Cutoff chart to dynamically update the other charts. The ROC and other charts are standard measurements of the classifier.

  

• Math curve charts display a group of curves based on equations that you enter. You do not use a data set with this chart. Instead, you use it to compare the results with the data set in another chart, like the scatter plot chart.

  

• Sunburst charts display different depths of hierarchical groups. The Sunburst chart was formerly an option in the Treemap chart.

  

• Tree charts represent a hierarchy in a tree-like structure. The Tree chart consists of a root node, line connections called branches that represent the relationships and connections between the members, and leaf nodes that do not have child nodes. The Tree chart was formerly an option in the Treemap chart.

  

Week ending 16 October 2020

Change to Watson Machine Learning deployment frameworks

The following changes to deployment frameworks might require user action.

Support for Python 3.7

You can now select Python 3.7 frameworks to train models and run Watson Machine Learning deployments. See Service plan changes and deprecations.

Deprecation of Python 3.6

Python 3.6 is being deprecated. Support will be discontinued on April 8, 2021. See Service plan changes and deprecations.

Support for Spark 3.0 and new language versions

• Spark 3.0

  • You can now select a Spark 3.0 environment to run notebooks with Python 3.7, R 3.6 and Scala 2.12 or to run notebook jobs.

• New languages

  • Python 3.7

    You can select Python 3.7 environments to run Jupyter notebooks (including with GPU) in Watson Studio.

    Python 3.6 is being deprecated. You can continue to use the Python 3.6 environments; however you will be notified that you should move to a Python 3.7 environment. When you switch to Python 3.7, you might need to update code in notebooks if the versions of open source libraries that you used are different in Python 3.7.

  • Scala 2.12

    With the introduction of Spark 3.0, you can start using Spark with Scala 2.12 in notebooks and jobs. Again, you might need to update code in your notebooks if library versions that you used with Scala 2.11 are not compatible in Scala 2.12.

Support ends for SPSS Modeler runtime 18.1 and certain Python nodes

Support for SPSS Modeler flows trained with 18.1 and containing certain Python nodes is discontinued as of October 14, 2020. See Service plan changes and deprecations.

Support ends for deployments based on deprecated AutoAI images (Watson Machine Learning)

Due to a known security vulnerability, AutoAI model deployments created using Watson Machine Learning on IBM Cloud prior to August 1, 2020 will be removed on November 1, 2020. See Service plan changes and deprecations.

Week ending 9 October 2020

Time series library for notebooks (Watson Studio)

You can now use the time series library to perform operations on time series data, including segmentation, forecasting, joins, transforms and reducers. You can use the time series library functions in Python notebooks that run with Spark. See Time series library.

Week ending 2 October 2020

Updates to Watson Knowledge Catalog offering plans

Starting 1 October, 2020, the Watson Knowledge Catalog offering plans have changes. See Service plan changes and deprecations.

Week ending 25 September 2020

Batch deployment available for AutoAI experiments (Watson Machine Learning)

Starting with the V2 Watson Machinne Learning service instance and the V4 Watson Machine Learning APIs, rolled out on September 1, 2020, batch deployment is supported for AutoAI experiments. For details, see Creating a batch deployment.

New Databases for EDB service

You can now create the Databases for EDB service from the Cloud Pak for Data as a Service services catalog to access EDB Postegres Advanced Server. See Databases for EDB.

Week ending 18 September 2020

New jobs user interface for running and scheduling Data Refinery flows and notebooks (Watson Studio)

The user interface gives you a unified view of the job information.

Data Refinery flow job

Notebook job

You create the job for a Data Refinery flow or for a notebook directly in the user interface for each tool or from the Assets page of a project. See Jobs in a project.

Week ending 11 September 2020

Change to Watson Machine Learning service credentials

The new V2 Watson Machine Learning service instance rolled out on September 1 uses new, simplified authentication. Obtaining bearer tokens from IAM is now performed using a generic user apikey instead of a Watson Machine Learning specific apikey. It is no longer necessary to create specific credentials on the Watson Machine Learning instance, so the Credentials page was removed from the IBM Cloud services catalog. For details, see Authentication.

During the migration period, you can use existing Watson Machine Learning service credentials to access your legacy V1 service instance and assets. Lite users of instances provisioned before Sept 1st 2020 can keep using existing credentials during the migration period but cannot generate new credentials. Standard and Professional plan users can follow the steps in Generating legacy Watson Machine Learning credentials to create new credentials.

Spark 2.3 deprecation (Watson Studio)

Starting 1 October, 2020, you must select a Spark 2.4 environment instead of a Spark 2.3 environment to run a notebook or job. See Service plan changes and deprecations.

Week ending 4 September 2020

New Watson Machine Learning service plans

Watson Machine Learning released new plans on IBM Cloud. These new plans accommodate and provide entitlements for the newest features and patterns available to Watson Machine Learning users, starting on September 1, 2020.  See Service plan changes and deprecations.

Upgrading to Watson Machine Learning “V2” Instances

All Lite plan users are automatically upgraded from v1 to v2 service plans. Lite plan users can now call the v4 APIs or use the v4 Python client library to conduct machine learning model training, model saving, and deployment, and access the newest features such as runtime software specifications for your deployments. See Service plan changes and deprecations.

Full support for v4 APIs and an updated Python client library (Watson Machine Learning)

The v4 APIs and Python client library are now generally available for use with the v2 service plans. The new APIs support features such as deployment spaces for organizing all of the assets required for running and managing deployment jobs, software specifications, and updated authentication. Note that support for v3 and v4 beta APIs ends on March 1, 2021. Review the differences between the v3, v4 beta, and v4 APIs.

Introducing deployment spaces (Watson Machine Learning)

Deployment spaces let you deploy and manage models and other operational assets such as data sources and software specifications in a separate environment from your projects. When your project assets are ready to deploy, you promote assets to your deployment space to configure deployments, test models and functions, consume scoring endpoints, and manage production jobs. Spaces, like projects, are collaborative, so you can invite others to collaborate and manage access for a space.

Watson Studio enhancements

Watson Studio now leverages the newest Watson Machine Learning APIs. Consequently, to take actions from the Watson Studio interface that require Watson Machine Learning, such as triggering AutoAI experiments, you must have a “v2” Watson Machine Learning instance associated with the project. Watson Studio projects that are still associated with an older Watson Machine Learning instance will display a message that instructs you to migrate your assets and associate a new v2 Watson Machine Learning instance. 

Additionally, starting on September 1st, Watson Studio users will be able to save models and other artifacts produced through use of Watson Machine Learning alongside other assets like notebooks in their Watson Studio project. For details on machine learning tools you can use to create project assets, see Machine Learning Overview.

Decision Optimization support for Watson Machine Learning

Decision Optimization supports all of the new features available with Watson Machine Learning, including using software and hardware specifications to configure optimization models and using deployment spaces to organize the assets required for deployment. For a complete list of changes, see Migrating from Watson Machine Learning API V4 Beta.

Decision Optimization enhancements (Watson Studio)

You can now use these enhancements to Decision Optimization:

• The Decision Optimization model builder now contains a new Overview pane which provides you with model, data and solution summary information for all your scenarios at a glance. From this view you can also open an information pane where you can create or choose your deployment space. See Overview pane.
• To create and run Optimization models you must have both a Machine Learning service added to your project and a deployment space associated with your experiment.
• You can now deploy models using Watson Machine Learning from the the Decision Optimization model builder scenario pane. See Scenario pane and Deploying a model using the user interface.
• A new sample Extend software specification notebook is now available which shows you how to extend the Decision Optimization software specification (runtimes with additional Python libraries for docplex models). See Python client examples and download the sample from Decision Optimization sample repository.
• The Explore solution view of the model builder has been updated to show more information about the objectives/KPIs, solution tables, constraint or bounds relaxations or conflicts, engine statistics and log. See Explore solution view.
• The Visualization view of the model builder now enables you to create Gantt charts for any type of data where it is meaningful and is no longer restricted to scheduling models only. See Visualizations view.

Translation of documentation

You can now read this documentation in these languages by setting your browser locale:

• Brazilian Portuguese
• Simplified Chinese
• Traditional Chinese
• French
• German
• Italian
• Spanish
• Japanese
• Korean
• Russian

Not all documentation topics are translated into all of these languages.

Jupyter notebook editor upgraded (Watson Studio)

The Jupyter notebook editor in Watson Studio is upgraded from Jupyter notebook version 6.0.3 to version 6.1.1. For a list of changes, including keyboard short-cut key changes, see Jupyter Notebook Changelog.

Week ending 21 August 2020

Enhanced Cognos Dashboards

You can now use these enhancements to the dashboards in a project:

• New visualizations, including Waterfall, KPI widget, and an enhanced cross-tab.
• A contextual toolbar and a data mapping panel.

See Cognos Dashboards.

Databases for MongoDB service

You can now provision a Databases for MongoDB service from the Services catalog.

See Databases for MongoDB.

Use Data Refinery to change the decimal and thousands grouping symbols in all applicable columns (Watson Studio, Watson Knowledge Catalog)

When you use the Convert column type GUI operation to detect and convert the data types for all the columns in a data asset, you can now also choose the decimal symbol and the thousands grouping symbol if the data is converted to an Integer or to a Decimal data type. Previously you had to select individual columns to specify the symbols.

See Convert column type in GUI operations in Data Refinery, under the FREQUENTLY USED category.

Week ending 14 August 2020

Google Cloud Platform integration

You can now configure an integration with the Google Cloud Platform (GCP) to access data sources from GCP.

See Integrating with Google Cloud Platform.

Week ending 31 July 2020

Security update for AutoAI deployments (Watson Machine Learning)

There is a known security vulnerability with the image used for AutoAI model deployments created using Watson Machine Learning on IBM Cloud prior to August 1, 2020. The image vulnerability has been addressed, so deployments of models created with AutoAI experiment after August 1, 2020 are not impacted. See Service plan changes and deprecations.

Removal of Neural Network Modeler and SparkML modeler

Both the beta Neural Network Modeler and the beta SparkML modeler tools are removed from Watson Studio.

Week ending 24 July 2020

Cloud Pak for Data as a Service is GA!

Cloud Pak for Data as a Service is now generally available. Sign up for Cloud Pak for Data as a Service at dataplatform.cloud.ibm.com.

Learn more about Cloud Pak for Data as a Service.

Read the Making IBM Cloud Pak for Data more accessible—as a service blog post.

Subscribe to Cloud Pak for Data as a Service

You can now upgrade your IBM Cloud account from a Lite plan by subscribing to Cloud Pak for Data as a Service. With a subscription, you commit to a minimum spending amount for a certain period of time and receive a discount on the overall cost compared to a Pay-As-You-Go account.

See Upgrading to a Cloud Pak for Data as a Service subscription account.

Learn quickly with a guided tutorial

You can quickly learn how to use tools in projects by taking a guided tutorial. A guided tutorial starts with a sample project that contains the data and anything else you need. After you create the project, the tutorial starts and guides you through the steps to solve a specific business problem.

Click the Take a guided tutorial link on your home page.

New services catalog

You can now create services IBM Cloud services that work with Cloud Pak for Data as a Service from the new services catalog. Select Services catalog from the main menu. You can see all your services by selecting the Your services option.

See IBM Cloud services.

Integrate with other cloud platforms

You can now configure integration with other cloud platforms to simplify creating connections to data sources in those cloud platforms in projects and catalogs. Select Cloud integration from the main menu. 

See Integrating with other cloud platforms.

Use the new Data Refinery “Union” operation to combine the rows from two data sets that share the same schema (Watson Studio, Watson Knowledge Catalog)

The Union operation is in the ORGANIZE category. For more information, see GUI operations in Data Refinery.

Automatically detect and convert date and timestamp data types (Watson Studio, Watson Knowledge Catalog)

When you open a file in Data Refinery, the Convert column type GUI operation is automatically applied as the first step if it detects any non-string data types in the data. Now date and timestamp data are detected and are automatically converted to inferred data types. You can change the automatic conversion for selected columns or undo the step. For information about the supported inferred date and timestamp formats, see Convert column type in GUI operations in Data Refinery, under the FREQUENTLY USED category.

Week ending 17 July 2020

New look, new features, new Cloud Pak for Data as a Service brand coming soon!

Starting on 21 July, you’ll see some changes to the Watson Studio, Watson Machine Learning, and Watson Knowledge Catalog services.

What is changing

Your home page will look different and shows more information.
You’ll have some new options on the main menu: 

• A Services catalog option where you can create IBM Cloud services that work with Watson Studio and Watson Knowledge Catalog. See IBM Cloud services.
• A Cloud integration option for configuring integrations to other cloud platforms to simplify creating connections to data sources in those cloud platforms. See Integrating with other cloud platforms.

What might change

Your product brand might change to Cloud Pak for Data. If you provisioned any services that work with Watson Studio besides Watson Machine Learning and Watson Knowledge Catalog, you’ll see Cloud Pak for Data as the product brand. See Relationships between the Watson Studio and Watson Knowledge Catalog services and Cloud Pak for Data as a Service.

What isn’t changing

Your service plans and billing for your IBM Cloud services remain the same.

See Cloud Pak for Data as a Service overview.

Week ending 10 July 2020

Upcoming removal of Neural Network Modeler and SparkML modeler

Both the beta Neural Network Modeler and the beta SparkML modeler tools will be removed from Watson Studio on July 31.

Easily add data from a Cognos Analytics connection to a notebook (Watson Studio)

You can now add data from a Cognos Analytics connection by using the Insert to code function for the connection within a notebook. See Data load support.

The Lineage page is renamed to Activities (Watson Knowledge Catalog)

The Lineage page that you can see when you open a data asset in a catalog or project is now called Activities. The information shown on this page remains the same.

Week ending 12 June 2020

Perform aggregate calculations on multiple columns in Data Refinery

Now you can select multiple columns in the Aggregate operation. Previously all aggregate calculations applied to one column.

For more information, see Aggregate in GUI operations in Data Refinery, under the ORGANIZE category.

Filter values in a Boolean column in Data Refinery

You can now use these operators in the Filter GUI operation to filter Boolean (logical) data:
Is false
Is true

For more information, see Filter in GUI operations in Data Refinery, under the FREQUENTLY USED category.

In addition, a new template for filtering by Boolean values has been added to the filter coding operation.

filter(`<column>`== <logical>) 

For more information about the filter templates, see Interactive code templates in Data Refinery.

Week ending 05 June 2020

SPSS Modeler flow properties (Watson Studio)

You can now set flow properties. For details, see Setting properties for flows.

Week ending 22 May 2020

AutoAI Auto-generated WML notebooks and SDK available in beta

You now have two options for saving an AutoAI pipeline as a notebook:

• WML notebook - Work with a trained model in an annotated notebook. You can review and update the code, view visualization, and deploy the model with Watson Machine Learning.
• AutoAI_lib notebook - View the Scikit-Learn source code for the trained model in a notebook. Does not require Watson Machine Learning.

Additionally, the Watson Machine Learning Python client has been extended to include an SDK for the WML notebook. For details, see Saving an AutoAI generated notebook. Note: These features are being offered as a beta and are subject to change.

Changes to the Watson Studio plans

Starting on May 19, 2020, the Watson Studio plans has changes. See Service plan changes and deprecations.

Week ending 01 May 2020

More Decision Optimization compute options (Watson Studio})

You now have more options that cost less when you run Decision Optimization jobs. You can choose from new, more powerful Decision Optimization environments. The basic Decision Optimization compute environment now consumes only five capacity unit hours (CUH) instead of 20 CUH. The new environments consume 6-13 CUH.

Read the More Decision Optimization Compute on Watson Studio at No Additional Cost blog post.

“PureData System for Analytics” connection renamed to “Netezza (PureData System for Analytics)”

The PureData System for Analytics connection is now the Netezza (PureData System for Analytics) connection. This change is to reflect the announcement of the new Netezza Performance Server for on premises and Cloud. Your previous settings for a connection to PureData System for Analytics remain the same. Only the connection name changed.

New visualization charts in Data Refinery (Watson Studio, Watson Knowledge Catalog)

Data Refinery introduces six new charts. To access the charts, click the Visualizations tab in Data Refinery, and then select the columns to visualize. The chart automatically updates as you refine the data.

• Bubble charts display each category in the groups as a bubble.

  

• Circle packing charts display hierarchical data as a set of nested areas.

  

• Multi-charts display up to four combinations of Bar, Line, Pie, and Scatter plot charts. You can show the same kind of chart more than once with different data. For example, two pie charts with data from different columns.

  

• Radar charts integrate three or more quantitative variables that are represented on axes (radii) into a single radial figure. Data is plotted on each axis and joined to adjacent axes by connecting lines. Radar charts are useful to show correlations and compare categorized data.

  

• Theme river charts use a specialized flow graph that shows changes over time.

  

• Time plot charts illustrate data points at successive intervals of time.

  

Week ending 24 April 2020

Synchronizing assets with Information Governance Catalog is discontinued (Watson Knowledge Catalog)

You can no longer automatically synchronize data assets between Information Governance Catalog and Watson Knowledge Catalog.

Week ending 17 April 2020

GPU environments for running notebooks are GA (Watson Studio)

GPU environments for running Jupyter notebooks with Python 3.6 are now generally available for the Watson Studio Standard and Enterprise plans. GPU environments are available in the Dallas IBM Cloud service region only.

With GPU environments, you can reduce the training time needed for compute-intensive machine learning models you create in a Jupyter notebook with Python 3.6. With more compute power, you can run more training iterations while fine-tuning your machine learning models.

See GPU environments.

Week ending 3 April 2020

Changes to Watson Studio Enterprise plan

On April 1, 2020, the Watson Studio Enterprise plan has changes. See Service plan changes and deprecations.

Week ending 27 March 2020

AutoAI Auto-generated notebooks available in beta

Save an AutoAI pipeline as a notebook so you can view all of the transformations that went into creating the pipeline. Use the autoai-lib reference as a guide to the transformations. This feature is being offered as a beta and is subject to change. For details, see Saving an AutoAI generated notebook.

Week ending 20 March 2020

Upcoming changes to Watson Machine Learning GPU plans

Starting on May 1, 2020, Watson Machine Learning will update the capacity units per hour for GPU capacity types. See Service plan changes and deprecations.

Week ending 13 March 2020

Custom security policies available for restricting downloads (Watson Machine Learning)

By default, Watson Machine Learning does not restrict external sites users can access as part of operations such as downloading data source files or installing Python library packages. If you would like to limit access to a list of approved sites, contact IBM Cloud support to request a custom network policy for your organization.

New capabilities in AutoAI Watson Machine Learning

The following features are new or enhanced in AutoAI:

• The limit on the size of a data source for an AutoAI experiment is increased from 100 MB to 1 GB.
• The number of pipelines generated for an experiment is increasing from four to eight, based on the two top performing algorithms. You can now also increase the number of top performing algorithms to use for generating pipelines if you want to view and compare more pipelines. Each algorithm creates four optimized pipelines. For details, see Building an AutoAI model.

Week ending 06 March 2020

Updates to Watson Machine Learning frameworks

Support is now available for TensorFlow 1.15 and Keras version 2.2.5 for training and deploying models. See Service plan changes and deprecations.

Week ending 07 February 2020

New Spark and R runtime enabled for jobs in Data Refinery

You can now select Default Spark 2.4 & R 3.6 when you select an environment definition for a new job. The new runtime uses the same capacity unit hours (CUHs) as the Default Spark R 3.4 (which is Spark 2.3) runtime.

SAV files

SPSS Statistics .sav data files are now supported for import or export in SPSS Modeler.

Exercise more control over pipeline creation for an AutoAI experiment

You now have the option of specifying which algorithms AutoAI should consider for an experiment and how many of the top performing algorithms to use for creating pipelines. For details, see Building an AutoAI model.

Week ending 10 January 2020

“Hortonworks HDFS” connection renamed to “Apache HDFS”

The Hortonworks HDFS connection is now the Apache HDFS via the WebHDFS API connection. Your previous settings for connections to Hortonworks HDFS remain the same. Only the connection name has changed.

Geospatio-temporal library for notebooks (Watson Studio)

You can use the geospatio-temporal library to expand your data science analysis to include location analytics in your Python notebooks that run with Spark. See Using the geospatio-temporal library.

Older what’s new entries

• What’s new for 2019
• What’s new for 2018
• What’s new for 2017