Today, we are excited to unveil a new suite of AI offerings for IBM Z that are designed to help clients improve business outcomes by speeding the implementation of enterprise AI on IBM Z across a wide variety of use cases and industries. We are bringing artificial intelligence (AI) to emerging use cases that our clients (like Swiss insurance provider La Mobilière) have begun exploring, such as enhancing the accuracy of insurance policy recommendations, increasing the accuracy and timeliness of anti-money laundering controls, and further reducing risk exposure for financial services companies.
The new offerings are designed to help accelerate the adoption of popular AI frameworks and tooling. We are also announcing an enhanced Machine Learning for z/OS and advanced intelligence and operational improvements with the latest IBM z/OS operating system.
Our rich AI offerings—coupled with low-latency inferencing that leverages the IBM Telum on-chip AI accelerator—are designed to help clients integrate AI workloads into their most mission-critical business applications running on IBM Z and meet their throughput SLAs.
To help data scientists, developers and IT teams implement AI together, these toolkits for Z are designed to support clients as they aim to connect mainframe data and applications to open-source frameworks and packages. These tools include frameworks and libraries that are optimized and supported for IBM Z, and they are built to allow developers to start implementing trustworthy AI capabilities on z/OS. Supported by the same underpinnings for IBM watsonx.ai that empower data scientists and developers to build, run and manage machine learning models, these toolkits will evolve over time to become an integral part of the IBM watsonx portfolio.
We’ve committed to optimizing IBM Z for AI from the silicon up, starting with the IBM Telum processor. IBM z16 delivers an on-chip integrated accelerator for low-latency inferencing, backed by IBM Z resiliency and security. Focusing on reducing latency during inferencing allows clients to shorten the time to receive insights and recommendations from AI models.
“According to our research, we estimate that about 70% of global transactions, on a value basis, run on IBM Z,” said Neil Katkov, PhD, Director at Celent. “Applying fine-tuning and inferencing to mission-critical data at this scale is a powerful innovation for use cases across business and IT operations.”
Analyzing transactions at scale could mean that insurance companies can more quickly make insurance offer recommendations. For example, La Mobilière has depended on IBM Z to process high volumes of transactions and provide a secured environment for their most sensitive data. Leveraging SQL Data Insights on IBM z16 allowed them to uncover patterns in their mission-critical data to improve their insurance offer predictions and reduce cost.
There’s a lot of innovation happening with generative AI, including the recently announced IBM watsonx Code Assistant for Z—a new generative-AI-assisted product for mainframe application modernization that will help enable faster translation of COBOL to Java on IBM Z and enhance developer productivity.
However, for many businesses, the first step to deriving value from AI today means focusing on the entire AI lifecycle, which also includes the fine-tuning, inferencing and deployment of machine learning and deep learning models. For companies to make the most of their AI investments, we believe that they need to tap into their mission-critical data. IBM z16 is designed to score business transactions at scale delivering the capacity to process up to 300B deep learning inference requests per day with 1ms of latency.[1] This scale opens significant opportunities for AI use cases on the mainframe, such as fraud detection, anti-money laundering, clearing and settlement, healthcare and application modernization.
To learn more about the new suite of AI offerings and get started on your AI journey, visit the AI on IBM Z website, and read the recently published analyst paper from Karl Freund (link resides outside of ibm.com).
IBM’s plans, directions, and intentions may change or be withdrawn at any time at IBM’s discretion without notice. Information about potential future products and improvements is provided to give a general idea of IBM’s goals and objectives and should not be used in making a purchase decision. IBM is not obligated to provide any material, code, or functionality based on this information.
[1] Disclaimer: Performance result is extrapolated from IBM internal tests running local inference operations in a z16 LPAR with 48 IFLs and 128 GB memory on Ubuntu 20.04 (SMT mode) using a synthetic credit card fraud detection model (link resides outside of ibm.com) exploiting the Integrated Accelerator for AI. The benchmark was running with 8 parallel threads each pinned to the first core of a different chip. The lscpucommand was used to identify the core-chip topology. A batch size of 128 inference operations was used. Results may vary.