Kyndryl will incorporate Nvidia AI technologies into its Kyndryl Bridge platform to optimize AIOps services.
IT infrastructure provider Kyndryl has pumped up its portfolio of generative AI services by announcing a partnership with Nvidia to simplify genAI deployments for network and application management. It’s also offering new AI-driven mainframe modernization consulting and managed delivery offerings.
In terms of collaborating with Nvidia, Kyndryl said it will incorporate Nvidia NIM inference microservices into its Kyndryl Bridge integration platform to enable AIOps services to be optimized on Nvidia Tensor Core GPUs; the combination of technologies is aimed at allowing enterprises to quickly process failure prediction and analysis and significantly reduce network and IT infrastructure failures.
Kyndryl Bridge integrates disparate management, observability and automation tools while making use of AI and ML to analyze the aggregated data and provide IT operations teams with the intelligence they need to keep systems running at peak performance. NIM is a collection of AI-based microservices that can be deployed on any cloud or in a data center to help enterprises deploy generative AI across a variety of models and infrastructures, according to Nvidia.
In addition, Kyndryl will incorporate retrieval-augmented generation (RAG) with Nvidia NeMo Retriever microservices to allow enterprise customers to tailor the package for specific management tasks, according to Kyndryl. The NeMo cloud-native platform lets users build, customize, and deploy generative AI models, and it includes training and inferencing frameworks, data curation tools, and pretrained models.
The vendors envision a number of use cases for the collaborative AI effort, such as establishing proper workload placement, operations automation, fraud and loss prevention, and real-time analytics.
“By combining Nvidia’s generative AI software with Kyndryl’s capabilities, we’re uniquely prepared to help address and resolve the biggest pain points for customers seeking to integrate AI across their hybrid IT estates,” said Hidayatullah Shaikh, vice president, software engineering, Kyndryl Bridge, in a statement.
In addition to the Bridge integration, Kyndryl Consult will support customer operations running on the Nvidia infrastructure, in on-premises deployments, or in a private cloud, hybrid or multicloud environments, the companies stated.
Using AI to transform mainframe environments
While GPU operations are the focus of the Nvidia collaboration, Kyndryl also said it will expand its AI-driven mainframe modernization services. The new AI services are aimed at furthering Kyndryl’s strategy of offering customers options when it comes to mainframe modernization – whether they want to keep data on the mainframe, migrate it off, or create a hybrid environment.
Kyndryl will now offer Bridge-based AI and generative AI tools for moving workloads off the mainframe to the cloud via generative AI-produced and Kyndryl-enhanced application documentation. The services will support automated conversion of classic mainframe application code to modern languages such Java.
Additionally, new AI-based services will help secure access to mainframe data used in cloud-based AI offerings, enable interoperability between mainframe and cloud operations, and assist in determining proper workload placement, Kyndryl stated.
“The AI-driven operational insights can enable more proactive and predictive management of mainframe systems, and provide visibility and control over mainframe performance and costs,” wrote Petra Goude, global practice leader for core enterprise & zCloud at Kyndryl, in a blog about AI and the mainframe.
If customers are keeping data on the Big Iron, the AI tools can determine application code modernization techniques to avoid potential production issues. The tools will integrate real-time AI into existing mainframe applications for better business insights, according to Kyndryl.
“The convergence of AI, mainframe and cloud is shaping the evolution of IT. The world’s biggest financial institutions, manufacturers and healthcare providers have relied on the mainframe and classic programming languages like COBOL, PL/I and REXX since the beginning of enterprise computing,” Goude wrote. “The mainframe continues to serve these companies well. But the very nature of the mainframe – what enables its reliability, resilience and security capabilities – contributes to difficulties with systems modernization. This is where AI and generative AI can help.
Embedding AI into mainframe and hybrid cloud environments can help augment human capabilities, streamline business process automation, and generate actionable insights from data, Goude stated.
“In addition, organizations can optimize services delivery and hardware and software costs by implementing and deploying AI-enabled chatbots and other operational processes to help execute day-to-day operations and recommend technology best practices,” Goude wrote. “And running AI models on the mainframe can provide insights that can help companies enhance customer satisfaction and compliance with regulatory requirements and potentially reduce fraud losses.”
