On Hybrid Cloud Infrastructure. Q&A with Luca Olivari
Q1 What are the main problems most organizations face when managing applications and data across multiple clouds and on-premises?
There are two main problems organizations face when managing applications and data across multiple clouds and on-premises data centers. The first is operational complexity. Each environment often has its own operating model, meaning organizations need to learn different skills, processes, and tools to manage each environment. This is inefficient and can get expensive. It can also lead to the second main problem, which is that complexity increases the chances of operational or security failures. It is difficult to apply security and compliance best practices across disparate operating environments.
Q2. What is most severe, operational complexity, increased costs or security risks?
Well, they are all interrelated. Operational complexity is the root issue, which leads to increased costs in the form of funding to staff large teams of specialists for each of the operating environments, licensing tools used to manage each environment, and opportunity costs – what else could you be doing to differentiate your business instead of managing cloud complexity? The security risks are a byproduct of the operational complexity. If I had to pick one that is most severe, I’d say it’s probably the opportunity costs. Organizations can spend so much time and money trying to manage their operating environments that they don’t invest enough in innovation, which is what ultimately is going to differentiate you as a business and lead to revenue and profit growth.
Q3. How does Nutanix`s approach to hybrid cloud infrastructure solve some of these challenges?
From an operational complexity perspective, Nutanix Cloud Platform (NCP) provides a single platform to run apps and manage data anywhere. We think of cloud more as an operating model than a destination. With NCP, you get a cloud operating model that offers consistency for any workload hosted across on-premises, edge and multiple public clouds. For example, with NCP and Nutanix Database Servicecustomers can run and manage all their databases from a single control plane across on-premises, public clouds, and edge locations. This allows developers to quickly provision new databases for appdev both on-premises and in the cloud, helps DBAs use automation to patch and backup databases, and provides the consistent performance and scalability you get from running all your databases on the same modern Nutanix hybrid multicloud infrastructure across environments. This results in significantly less operational complexity, helps to rein in costs, and includes built-in security that not only prevents and detects security threats but also helps to prevent data loss and ensures continuous business operations.
Q4. You have recently launched of Nutanix GPT-in-a-Box to simplify adoption of generative AI among enterprises. What is it and what is it useful for?
Nutanix GPT-in-a-Box is a great solution for customers looking to jump-start artificial intelligence (AI) and machine learning (ML) innovation while maintaining full control over the data. This new offering is a full-stack software-defined AI-ready platform, along with services to help organizations size and configure hardware infrastructure building blocks suitable to deploy a curated set of large language models (LLMs) using the leading open source AI and MLOps frameworks on the Nutanix Cloud Platform.
Nutanix GPT-in-a-Box includes all the necessary elements needed to build an AI-ready infrastructure. This includes Nutanix software (Nutanix Cloud Infrastructure, the AHV hypervisor, Kubernetes, and Files or Objects storage) along with a set of key open source elements to deploy and run AI workloads including, PyTorch as an AI framework and Kubeflow for MLOps.. In addition, Nutanix services will help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of LLMs, including Llama2, Falcon and MPT.
Q5. Generative AI (including Chat-GPT and LLMs) is top of mind for many enterprises, but most don’t have the in house resources to fully take advantage of this opportunity. How can your offering help here?
Many enterprises are grappling with how to quickly, efficiently, and securely take advantage of generative AI and AI/ML applications, especially for use cases where data sovereignty, governance and privacy are top priorities.
Organizations looking to build an AI-ready stack often struggle with how to best support MLOps administrators and data scientists, while the prospect of large AI investment costs has enterprises stalled in their AI and ML strategy.
Nutanix GPT-In-a-Box allows customers to easily size, configure, and purchase AI-ready infrastructure to fine-tune and run generative pre-trained transformers (GPT) and LLMs.
Q6. Can you give us some examples how customers are using GPT-in-a-Box?
We’re still in the early days for GPT-in-a-Box but are already seeing interest from customers. Recently, a large federal agency in the US selected Nutanix GPT-in-a-Box to run large language models to analyze text and identify trends and anomalies.
Qx. Anything else you wish to add?
Organizations everywhere are struggling with an increasingly complex reality, managing applications and data across multiple public clouds, on-premises and at the edge. A hybrid multicloud strategy enables them to address many of these challenges while maintaining the flexibility and agility needed to quickly adapt to changing business needs.
Luca Olivari is Global Nutanix Database Service Sales Leader, Nutanix