Earlier this month, Vultr participated in the Las Vegas Gartner IT Infrastructure, Operations & Cloud Strategies (IOCS) Conference. Many Gartner analysts and vendors presented a range of topics focused on the future of IOCS. While the conference touched on many important themes, we’re looking at three key insights enterprises must address starting on January 2, 2024.
1. Success or failure with Generative AI will determine the success or failure of your business.
Enterprises in all industries are racing against time and their competitors to undergo AI transformation. Yet the past year has shown that enterprises must pursue generative change to thrive in the new AI-driven business reality.
Generative transformation encompasses the technological and organizational changes required to position generative AI at the core of as many business practices as possible. While the devil is in the details, these three foundational steps comprise the generative transformation journey:
- Formulate a strategy around how your enterprise will use generative AI. Plan how generative AI will improve your operations to support your business objectives. Establish metrics to measure the effectiveness of your productive AI initiatives. Review and revise monthly.
- Plan for the organizational change needed to fully roll out generative transformation. Generative transformation requires more than a technology shift; it requires an organization-wide mindset shift. Emphasize staff augmentation, not staff replacement. Establish a culture of responsible AI at all phases of the large language model operations (LLMOps) lifecycle.
- Build and deploy a platform engineering solution to empower the IT, Operations, and Data Science teams supporting your generative transformation journey. Platform engineering offers self-serve access to the infrastructure, tools, and services teams need to design, deploy, and scale the optimal cloud CPU and GPU stacks to support the enterprise’s DevOps, AI, and ML operations. (More on platform engineering below.)
How Vultr supports generative transformation
- Vultr provides an array of best-in-class NVIDIA GPUs, including the brand-new NVIDIA GH200 superchip and the H100, A100, L40S, A40, and A16. This unparalleled collection of industry-leading GPUs streamlines operations so teams can deploy the right GPUs and associated tools and services in any of our 32 global data center locations, optimizing each step of the generative AI lifecycle.
- Vultr espouses the principles of responsible AI and prioritizes data security and compliance throughout its own business practices and supports its customers pursuing the same. We work with industry leaders to ensure safety and security in all phases of machine learning operations (MLOps) lifecycle, including testing, fine-tuning, deployment, consumption, and monitoring of generative AI and other machine learning models.
- Vultr enables a platform engineering approach. We help enterprises scale generative AI operations close to customers and wherever the enterprise operates. For distributed enterprises, efficient LLMOps involves developing LLMs centrally, training LLMs regionally, and producing inferences locally to preserve data governance and security while maximizing user experience and reducing costs.
2) With robust platform engineering solutions underpinning LLMOps, enterprises can reap returns on their generative AI investments.
Adopting a platform engineering approach empowers the internal teams contributing to the enterprise’s generative transformation journey. The tenets of platform engineering are designed to amplify the value of DevOps and ML engineers by paving the way for them to get to the business-sc :
- Provide a high-quality, self-serve user experience so developers and ML engineers can move quickly to assemble the ideal environments to produce generative AI applications and supporting structures.
- Treat infrastructure platform engineering as a product. Assign a dedicated project team to develop and maintain the platform. Beyond initial deployment, this requires setting measurable goals and collecting metrics to track platform performance regarding availability, reliability, self-serve efficiency, and time to value.
- Build your platform engineering solution around the principles of composability, ensuring open, interoperable, modular components that can be swapped in or out as requirements change. This improves observability, discoverability, and scalability and prioritizes automation and orchestration.
How Vultr supports platform engineering
- Vultr offers self-serve and automation to make spinning up complete dev environments supporting AI operations fast, easy, and affordable. Vultr recognizes the importance of the time-to-value metric, making cloud CPU and GPU infrastructure available in a few clicks. Vultr takes care of the configurations, leaving developers and ML engineers to focus on the high-value work of creating the platform engineering solutions and the next great generative AI application.
- Vultr embraces and evangelizes composability in our cloud CPU, GPU stacks, and related solutions. These include the infrastructure, tools, services, and applications needed to build and maintain a robust platform engineering solution. Composability yields ultimate flexibility and maximal cost efficiency without vendor lock-in. These attributes support efficient, flexible platform engineering and LLMOps initiatives across the enterprise.
- The Vultr Cloud Alliance and Vultr Marketplace are one-stop shops for like-minded vendors offering composable, interoperable solutions that integrate seamlessly on Vultr cloud infrastructure. Vultr’s vast cloud infrastructure options, combined with the tools and applications available in the Vultr Marketplace, make it easy for enterprises to build and deploy platform engineering solutions that underpin successful generative AI operations.
3) Enterprises need a strategic edge computing strategy to delight customers while optimizing their infrastructure operations.
While edge computing (in all its varying names) is not a new concept, many enterprises lack a strong edge strategy. And even fewer are thinking now of how generative AI fits into an edge strategy, but everyone should be. Given the four incontrovertible aspects of edge compute – physics (speed of light/ latency), economy (cost of bandwidth), Murphy's Law (connections go down), and law of the land (data regulations) – serving inference at the edge will soon be table stakes:
- Enterprises’ generative transformation initiatives must evolve to generate and serve local inference. Organizations need a distributed, composable cloud GPU stack to train and fine-tune models geographically removed from where users interact with generative AI applications. Models must then be fine-tuned to respect regional norms and cultures and comply with local data governance and privacy regulations.
- The nature of content at the edge is constantly evolving. Instead of serving end users canned, static content, enterprises will position generative AI applications tuned to local data to produce and serve dynamic content at the edge.
How Vultr supports edge strategies
- Vultr offers a complete, composable, cloud GPU stack so enterprises can manage every aspect of the LLM lifecycle, from building LLMs or harnessing publicly available open-source LLMs, to training LLMs, to fine-tuning and testing, to deploying them globally across 32 data center locations on six continents, to monitoring and maintaining LLM performance.
- Vultr’s embrace of composability means that enterprises will always have the flexibility to reconfigure their GPU stacks as the technology advances and business needs change.
Looking forward: Lean on Vultr to support your generative transformation
The Gartner conference shed light on the critical role of generative AI, platform engineering, and edge computing in shaping the future of enterprises. The three key takeaways emphasize the need to place generative AI at the heart of AI transformation, backed by strategic planning, platform engineering, and organizational mindset shifts.
Vultr's commitment to providing top-tier NVIDIA GPUs across our global network of data center locations, advocating for self-serve and automation through interoperable cloud CPU and GPU infrastructures, and supporting composability underscores our role in facilitating successful generative transformation and AI operations, providing enterprises with the tools and flexibility needed to thrive in this dynamic environment.