May 17, 2021 By Barry Baker 2 min read

A clear trend is emerging in the era of hybrid cloud: winning enterprises will likely pull ahead by scaling the value of their data with AI.

For many IBM Z® & IBM LinuxONE customers, the enterprise platform often serves as the system of record for their mission-critical data and applications. Data scientists often look for open-source solutions, and we are committed to embracing and bringing open-source AI capabilities to Z and LinuxONE that can support real-time AI decision-making at scale.

Today, IBM and Anaconda, provider of the leading Python data science platform, are announcing the general availability of Anaconda for IBM Linux on Z & LinuxONE. Anaconda on Linux on Z & LinuxONE is the latest step toward bringing popular data science frameworks and libraries to these enterprise platforms, providing a consistent data science user experience across the hybrid cloud.

Data scientists who already know and love Anaconda can now expand their open-source data science experience to include IBM Z & LinuxONE, while continuing to work with their favorite tools and frameworks like conda, XGBoost and SciKit-Learn. This expands and enables choice in AI frameworks and tooling for end-to-end data science directly on the platform, including development, training, testing and production. Data scientists can benefit from the security capabilities, high availability and scalability of the IBM Z & LinuxONE platforms when implementing AI deployments targeting time-sensitive workloads or transactions when they are taking place. Anaconda runs natively on Linux on IBM Z, and through z/OS Container Extensions (zCX) on z/OS, the solution brings open-source data science tools close to key workloads, leveraging the data gravity of the Z and LinuxONE platforms.

According to new research commissioned by IBM in partnership with Morning Consult, 90% of respondents said that being able to build and run AI projects wherever their data resides is important[1].  Workloads running on IBM Z & LinuxONE often need to adhere to strict latency and SLA requirements to support transactions that are key to our modern life such as online purchases. With Anaconda for Linux on Z & LinuxONE, organizations can perform AI analysis in close proximity to their data, addressing latency to deliver insights where and when they are needed.

Customers can start using Anaconda Individual Edition and Anaconda Commercial Edition today by downloading the Individual Edition or Miniconda installer, and following the associated installation documentation:

For more information on using Anaconda Individual or Commercial Edition, please visit docs.anaconda.com.

[1] IBM Global AI Adoption Index 2021 (https://filecache.mediaroom.com/mr5mr_ibmnews/190846/IBM%27s%20Global%20AI%20Adoption%20Index%202021_Executive-Summary.pdf)

Was this article helpful?
YesNo

More from Cloud

IBM Tech Now: April 8, 2024

< 1 min read - ​Welcome IBM Tech Now, our video web series featuring the latest and greatest news and announcements in the world of technology. Make sure you subscribe to our YouTube channel to be notified every time a new IBM Tech Now video is published. IBM Tech Now: Episode 96 On this episode, we're covering the following topics: IBM Cloud Logs A collaboration with IBM watsonx.ai and Anaconda IBM offerings in the G2 Spring Reports Stay plugged in You can check out the…

The advantages and disadvantages of private cloud 

6 min read - The popularity of private cloud is growing, primarily driven by the need for greater data security. Across industries like education, retail and government, organizations are choosing private cloud settings to conduct business use cases involving workloads with sensitive information and to comply with data privacy and compliance needs. In a report from Technavio (link resides outside ibm.com), the private cloud services market size is estimated to grow at a CAGR of 26.71% between 2023 and 2028, and it is forecast to increase by…

Optimize observability with IBM Cloud Logs to help improve infrastructure and app performance

5 min read - There is a dilemma facing infrastructure and app performance—as workloads generate an expanding amount of observability data, it puts increased pressure on collection tool abilities to process it all. The resulting data stress becomes expensive to manage and makes it harder to obtain actionable insights from the data itself, making it harder to have fast, effective, and cost-efficient performance management. A recent IDC study found that 57% of large enterprises are either collecting too much or too little observability data.…

IBM Newsletters

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