In the DC conversation, Suse Director and CTO Vishal Ghariwala shares the expertise on the benefits of open source AI, the importance of security and scalability, and the future of AI adoption in businesses.
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What is Suse's expertise in the high-tech industry?
Suse is a multinational German open source software company that has been around since 1992. We are the global leader in innovative, reliable, enterprise-grade open source solutions, including Enterprise Linux, Kubernetes Management, and Edge solutions. What sets us apart is our commitment to open source, which we have been defending since the early 90s. Our software is designed to enable customers to innovate from the data center to the cloud, the edge and beyond. With operations worldwide, including Europe, North America, Latin America and the Asia-Pacific region, he serves more than 60% of the Fortune 500, demonstrating the power of mission-critical workloads.
Why is open source important for companies that employ AI technology?
Open source is important for companies that adopt AI technology for several reasons. First, it promotes rapid innovation across multiple sectors, allowing impossible joint advancements with a single vendor dominating the space. Second, open source promotes transparency and trust through community-driven development. This is especially important for AI where understanding the decision-making process and mitigating bias is important. Finally, open source offers businesses the flexibility to select and adapt their technology as needed, without being trapped in a particular vendor or pricing structure. This is essential for a rapidly evolving AI landscape.
How does Suse's platform help businesses manage and scale their AI workloads?
Suse's platform helps businesses manage and scale their AI workloads by providing a robust, open infrastructure that supports scalability, security and flexibility. A cloud-native Kubernetes solution, the Rancher Prime platform enables efficient scaling and management of AI applications across a wide range of environments, including on-premises, cloud, and edge. With Rancher Prime, you can get one glass pane for managing different environments, allowing you to seamlessly scale your AI workloads while maintaining control and security of sensitive data.
Vishal Galiwara, director and CTO
What are the advantages of using containers instead of virtual machines for app deployment?
Containers offer three important advantages over traditional virtual machines: Transportability, scalability, and business agility. Containers provide portability by allowing applications and their dependencies to be packaged and run consistently in different environments. Also, because of its lightweight nature, the containers allow for scalability, allowing for easy spin-up and lowering resources when needed. Finally, containers promote business agility by enabling faster application development, testing and deployment via a microservices architecture that allows each service to be updated independently without affecting the entire application.
Can you explain how Suse's open source solutions simplify the migration to a cloud-native environment?
Suse's open source solutions simplify the migration to a cloud-native environment by addressing the top three challenges facing customers: frastructural complexity, development team burdens and security concerns. Rancher Prime Platform provides a common control kit for infrastructure teams to manage operations across multiple environments. It also abstracts complexity and allows development teams to focus on creating applications without worrying about the underlying infrastructure. Additionally, Rancher Prime integrates security and observability capabilities to help security teams drive compliance, address vulnerabilities, and efficiently resolve reliability issues.
Do you have any advice for companies looking to maintain their technology stack in the future to advance AI?
The future technology stack of AI advances requires companies to envelop open ecosystems and focus on three key areas: prioritizing security and observability, and adopting scalable architectures. An open ecosystem that includes open source LLMS and infrastructure ensures scalability and flexibility, enabling businesses to adapt to changing technology environments. Security and observability are important to protect sensitive data and ensure reliability and explainability of AI output. Finally, a scalable architecture is essential to supporting the resource-intensive nature of AI workloads and ensuring high availability and performance.
