Service plan changes and deprecations

You can view the history of service plan changes and deprecations for 2020 and 2021.

Watson Studio

Removal of data annotation with Defined Crowd and Figure Eight (16 August 2021)

Starting immediately, you can no longer use the third-party crowd annotation platforms of Defined Crowd or Figure Eight to create annotation jobs.

Number of Spark executors restricted by service plan (23 July 2021)

Watson Studio Lite plan users can 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.

Connections are supported on all Watson Studio offering plans (02 April 2021)

Previously these connections were limited to Watson Studio Standard or Enterprise plans. Now these connections are available on all plans:

  • Dropbox
  • Looker
  • OData
  • SAP OData
  • Tableau

Deprecation of Python 2.7 and 3.5 (20 January 2021)

Python 3.6 deprecated and are no longer available as of January 20, 2021. The default version of Python in Watson Studio is now 3.7. When you switch from Python 3.6 to Python 3.7, you might need to update your code if the versions of open source libraries that you use are different in Python 3.7. See Changing the environment.

Spark 2.3 deprecation (1 October 2020)

Starting 1 October, 2020, you can no longer select a Spark 2.3 environment to run a notebook or job. You must select a Spark 2.4 or 3.0 environment instead. Existing notebooks and jobs with Spark 2.3 environments stopped running on 30 November, 2020.

Removal of SparkML modeler and Neural Network Modeler (31 July 2020)

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

Changes to the Watson Studio plans (19 May 2020)

Starting on May 19, 2020, the Watson Studio plans has the following changes:

  • All plans: The free compute environment is no longer available. All your compute usage now consumes capacity unit hours. The Lite plan has a limit of 50 capacity unit hours per month.
  • Lite and Standard plans: Compute environments provided by associated services, such as IBM Analytics Engine, are now available only with the Enterprise plan.
  • Lite plan: Only the smallest size Spark environments are now available for Lite plans, with 2 executors that each have 1 vCPU and 4 GB RAM, and one driver that has 1 vCPU and 4 GB RAM. Large compute environments with 8 or more vCPU are no longer available for the Lite plan.
  • Lite plan: The ability to export projects now requires the Standard or Enterprise plan.

This change was first announced on March 17 in this Watson Studio Plan Updates blog post.

Changes to Watson Studio Enterprise plan (1 April 2020)

On April 1, 2020, the Watson Studio Enterprise plan has the following changes:

  • The number of free authorized users is now 10.
  • The cost of adding extra authorized users is reduced by 50%.
  • The compute usage rate is reduced to $0.40 USD per capacity unit hour used beyond the 5000 CUH per month that are included in the plan.

Read the Watson Studio plan update blog post.

Watson Machine Learning

GPU environments adjustment in Watson Machine Learning (12 March 2021)

Starting on March 19, 2021, GPU environments in Watson Machine Learning will be available only through v2 Standard plan and v2 Professional plan.

After the adjustment, users new to IBM Cloud can get a free trial of GPU environments from Watson Machine Learning with the following steps:

  1. Get $200 credit through upgrading to an IBM Cloud Pay-As-You-Go account.
  2. Create a Watson Machine Learning v2 Standard plan instance in IBM Cloud catalog.
  3. Start a deep learning experiment.

Note that a v2 Standard plan might incur charges if there are jobs running after the credit runs out or expires after 30 days. 

For details, see Watson Machine Learning plans.

Removal of Python 3.6 environment (Watson Studio and Watson Machine Learning) (8 April 2021)

Python 3.6 was removed from Watson Studio and Watson Machine Learning due to a security vulnerability.

Starting on April 8, 2021, you can’t create or run assets such as notebooks, machine learning models, Decision Optimization solutions, or Python functions that are based on Python 3.6 environments or frameworks. Update existing assets to use Python 3.7 environments or frameworks instead. For details on changing notebook environments, see Environments. For details on machine learning frameworks, see Supported frameworks.

Support for V1 machine learning instances and deprecated APIs ended (April 8, 2021)

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. Note the following:

  • V1 machine learning service instances were created prior to September 1, 2020. Service instances created after that date are V2 instances. For details, see Creating a service instance.
  • If you have not already migrated and redeployed your assets, do so prior to April 8, 2021. After that date, you will get errors when scoring deployed assets that rely on deprecated V3 and V4-beta Watson Machine Learning APIs. For details, see Migrating Assets.
  • Create new asset deployments based on a V2 service instance and V4 Watson Machine Learning APIs.
  • Updating assets might require code changes. For details, see API changes for Watson Machine Learning.

Legacy APIs no longer supported for Watson Machine Learning Lite plan users (28 October 2020)

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. As a result, Lite users will get errors when they score deployed assets that rely on deprecated APIs. Create new deployments based on V4 Watson Machine Learning v4 APIs. For details, see API changes for Watson Machine Learning.

Change to Watson Machine Learning deployment frameworks (16 October 2020)

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. Until then, you can continue to use the Python 3.6 frameworks; however you will be notified that you should move to a Python 3.7 framework. For details on migrating an asset to a supported framework, see Supported frameworks.

Support ends for SPSS Modeler runtime 18.1 and certain Python nodes (16 October 2020)

Support for SPSS Modeler flows trained with 18.1 and containing certain Python nodes is discontinued as of October 14, 2020. If your SPSS models uses any of these Python nodes, then you must retrain your models using Watson Studio Canvas or any tool that uses SPSS Modeler 18.2 version. For details, see Supported frameworks.

Support ends for deployments based on deprecated AutoAI images (16 October 2020)

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. If you have not already migrated and redeployed your AutoAI models, do so prior to November 1, 2020. For details, see Migrating Assets.

New Watson Machine Learning service plans (4 September 2020)

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.  Plans created after September 1, 2020 are considered to be “V2” plans that are based on CUH consumption, replacing “V1” plans that billed for predictions. During the migration period, you will continue to be billed only for V1 access while you evaluate the new V2 service instance plans and features. The migration period for Lite users is over. The migration period for Standard and Professional users ends on April 8, 2021.

New sign-ups will receive the latest plans and API entitlements; no Watson Machine Learning instances that correspond to the older plans can be provisioned. By April 8, 2021, only Watson Machine Learning instances which correspond to the updated plans will be supported. The following sections introduce the new features and describe how to best plan your migration. 

Upgrading to Watson Machine Learning “V2” Instances (4 September 2020)

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. 

For Standard plan and Professional plan users, you can choose when to migrate your assets for use with the V2 machine learning service instance. Users of these plan instances will have more time to work with both the older and the newer API sets and plan instances until April 8 2021. For details on working with a deprecated service instance, see Generating legacy Watson Machine Learning credentials.

Note: During the migration period, you will not be charged for your usage associated with the new plan instances while your V1 plan instances are still active.

Get details about the Watson Machine Learning plans.

Full support for v4 APIs and an updated Python client library (4 September 2020)

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 April 8, 2021. Review the differences between the v3, v4 beta, and v4 APIs.

Migration assistance for Watson Machine Learning (4 September 2020)

Watson Machine Learning users can easily migrate their Watson Machine Learning repository assets, such as machine learning models to Watson Studio Projects with automated assistance from a graphical migration tool, or programmatically, using a dedicated set of APIs. 

Watson Machine Learning migration action plan (4 September 2020)

Follow these steps to migrate assets and upgrade your service instance to take advantage of the new Watson Machine Learning plans and features.

  1. Review the updated Watson Machine Learning plans and consider which level of service is right for you.
  2. Migrate your assets, using the Migration Assistant tool from Watson Studio or using a programmatic solution.
  3. Start using your new machine learning service instance.
  4. Retrain models or update your Python functions, as needed.
  5. Create a deployment space and start to work with your migrated assets.
  6. Delete your old v1 service instance.

Spark 2.3 framework for Watson Machine Learning deprecated (4 September 2020)

Spark 2.3 framework for Watson Machine Learning is deprecated and will be removed on December 1, 2020. Use Spark 2.4 instead. For details, see Supported frameworks.

Compatibility issue for SPSS Modeler runtime 18.1 and older Python version (4 September 2020)

Support for SPSS Modeler flows trained with 18.1 and containing certain Python nodes is deprecated. For existing deployments which are using these nodes, you can continue to score the deployments till October 1, 2020. If the SPSS models uses any of these Python nodes, then it will require retraining the model using Watson Studio Canvas or any tool that uses SPSS Modeler 18.2 version. For details, see Supported frameworks.

Security update for AutoAI deployments (31 July 2020)

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. The following remedies are available:

For Lite plan users

Impacted AutoAI deployments will be deprecated (stop working) on the September 1st, 2020. You can redeploy your models in August, then migrate them to a new deployment space in September, 2020.

For Standard and Professional plans users

Impacted AutoAI deployments will be deprecated (stop working) on November 1st, 2020. You can redeploy your models in August, then migrate them to a new deployment space in September-October, 2020.

Changes to Watson Machine Learning GPU plans (20 March 2020)

Starting on May 1, 2020, Watson Machine Learning updated the capacity units per hour for GPU capacity types, as follows:

Capacity Type Capacity units required per hour
1 NVIDIA K80 GPU 3
1 NVIDIA V100 GPU 10

Capacity units required per hour of multiple GPUs is calculated by the capacity units per hour on single GPU times the total number of GPUs. For details, read the Watson Machine Learning GPU Update blog post.

Updates to Watson Machine Learning frameworks (06 March 2020)

Support is now available for TensorFlow 1.15 and Keras version 2.2.5 for training and deploying models. Due to a security vunerability with certain TensorFlow versions, support for TensorFlow 1.13 and 1.14 along with Keras 2.1.6 and Keras 2.2.4 will be deprecated. Users will need to upgrade to Keras 2.2.5 and switch to TensorFlow 1.15 backend. For details on the changes, view this Watson Machine Learning Tensorflow support announcement. See Supported frameworks.

Watson Knowledge Catalog

New Enterprise plan (25 June 2021)

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: Deliver 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.

Deprecation of importing Information Governance Catalog assets (22 April 2021)

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.

Connections are supported on all Watson Knowledge Catalog offering plans (02 April 2021)

Previously these connections were limited to Watson Knowledge Catalog Standard or Professional plans. Now these connections are available on all plans:

  • Dropbox
  • Looker
  • OData
  • SAP OData
  • Tableau

The lite plan longer supports backups for disaster recovery (21 December 2021)

If you have the Watson Knowledge Catalog lite plan, your assets in projects and catalogs are no longer backed up for disaster recovery purposes.

Updates to Watson Knowledge Catalog offering plans (1 October 2020)

Starting 1 October 2020, the Watson Knowledge Catalog offering plans have these changes:

  • Lite plan: The maximum number of users is reduced to 2. Catalog and projects are not backed up.
  • Standard plan: The maximum number of assets is increased to 1000.
  • Standard and Professional plans: The cost for the plan, extra users, and extra CUH is changing.

If you already have the Lite or Standard plan, your existing assets and catalog users remain unchanged.

Read the Updates to Watson Knowledge Catalog blog post.

Synchronizing assets with Information Governance Catalog is discontinued (24 April 2020)

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

Watson OpenScale

New pricing plan for Watson OpenScale (31 January 2021)

Watson OpenScale has a new Standard v2 pricing plan that takes effect at the end of January 2021. The existing, old Standard plan is discontinued in the catalog. To help customers better manage their paid usage, the new Standard v2 plan charges $250 USD per model per month.

Deprecated APIs (15 March 2021)

The use of the V1 API and SDK are deprecated and will be disabled as of April 8, 2021. For information about the V2 REST API, see IBM Watson OpenScale API Documentation. For information about the V2 Python SDK, see IBM Watson OpenScale Python SDK 3.0.3 documentation. For help with updating notebooks, see Updated V2 REST API and Python SDK.

Streaming Analytics

Streams flow tool is removed (5 February 2021)

The streams flows tool is removed from Watson Studio projects. For future support for streams flow use cases, see Streaming Analytics on IBM Cloud.

Visual Recognition

IBM Watson Visual Recognition is discontinued in Watson Machine Learning (7 January 2021)

IBM Watson Visual Recognition is discontinued. Existing instances are supported until 1 December 2021, but as of 7 January 2021, you can’t create instances. Any instance that is provisioned on 1 December 2021 will be deleted.