Analytics
Announcements
August 13, 2019 By Yin Chen < 1 min read

There are two incoming changes in Watson Machine Learning.

Upgrade to TensorFlow 1.13 and deprecate older versions

Due to a recent security vulnerability for multiple TensorFlow versions, we decide to upgrade TensorFlow versions to 1.13 for Watson Machine Learning deployment and training runtimes and deprecate all unsecure TensorFlow versions, including 1.5 and 1.11.

If you currently have older TensorFlow models deployed in Watson Machine Learning, you will need to download the model, test it in TensorFlow 1.13 environment, and deploy it back to Watson Machine Learning. You may need to make minor modifications based on the TensorFlow version compatibility guide—in many cases, TensorFlow is backward compatible for your model.

Datetime format change in deployment API

As part of the improvements to the Watson Machine Learning service, we changed the datetime format returned from our API. This change will impact the users who are consuming V4 API-supported WML Python clients for creating deployments or jobs and parsing the datetime fields in the deployment- or jobs-related metadata.

The date format currently being returned in a GET response of /v4/deployments is:

yyyy-MM-dd'T'HH:mm:ssZZZZ

The new format is:

yyyy-MM-dd'T'HH:mm:ss.SSS'Z'

Here are the dates you need to know

  • TensorFlow 1.13 runtimes available: August 13, 2019
  • Older TensorFlow versions deprecation announcement: August 13, 2019
  • End of Life for older TensorFlow versions: September 30, 2019 

You can read more about working with Watson Machine Learning runtimes—including the new TensorFlow 1.13 runtime—in our documentation.

Yin Chen
Offering Manager, Watson Studio Deep Learning

More from Analytics
October 3, 2024

How IBM Data Product Hub helps you unlock business intelligence potential

4 min read - Business intelligence (BI) users often struggle to access the high-quality, relevant data necessary to inform strategic decision making. These professionals encounter a range of issues when attempting to source the data they need, including: Data accessibility issues: The inability to locate and access specific data due to its location in siloed systems or the need for multiple permissions, resulting in bottlenecks and delays. Inconsistent data quality: The uncertainty surrounding the accuracy, consistency and reliability of data pulled from various sources…

September 30, 2024

Watsonx.data introduces support for a suite of modern dataops tools

2 min read - We’re excited to announce that IBM® watsonx.data™ now supports a powerful suite of tools for the modern dataops stack: data-build-tool, Apache Airflow, and VSCode. With data build tool (dbt) compatibility for both Spark and Presto engines, automated orchestration through Apache Airflow, and an integrated development environment via VSCode, watsonx.data offers a new set of rich capabilities. These features empower teams to efficiently build, manage and orchestrate data pipelines. The challenge of complex data pipelines Organizations today face the challenge of…

September 17, 2024

IBM Planning Analytics: The scalable solution for enterprise growth

5 min read - Companies need powerful tools to handle complex financial planning. At IBM, we've developed Planning Analytics, a revolutionary solution that transforms how organizations approach planning and analytics. With robust features and unparalleled scalability, IBM Planning Analytics is the preferred choice for businesses worldwide. We’ll explore the aspects of IBM Planning Analytics that set it apart in the enterprise performance management landscape. We delve into its architecture, scalability and core technology, highlighting its data handling capabilities and modeling flexibility.We'll also showcase its…

IBM Newsletters

 Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters