This article is part of a VB Lab Insights series on AI sponsored by Microsoft and Nvidia.
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To create value and business growth, organizations need to accelerate AI production at scale. Join experts from Microsoft and NVIDIA to learn how the right AI infrastructure helps lower barriers to adoption, control expenses, speed time-to-value and more.
Every enterprise technology wave of the last 20 years, from databases and virtualization to big data and others, has imparted a crucial lesson. AI – and the infrastructure that enables it – is no exception. To gain the traction and widespread adoption that can spark innovation requires standardization, cost management and governance. Unfortunately, many organizations today struggle with all three.
An eclectic and costly array of tools, models and technologies sprawl across many enterprises. Choices can vary from one data scientist or engineer to another. As a result, there’s no consistent experience. Working between groups and scaling pilots into production can be difficult.
Managing AI costs remains difficult for many enterprises and IT leaders. A new project can start inexpensively, but rapidly grow out of control. The cost of selecting, building and integrating robust, full-stack infrastructure needed for AI can quickly become a budget buster, especially in on-premises environments.
As for governance, AI efforts too often get siloed or spread across teams, groups and departments with no oversight from IT. That makes it difficult or impossible to determine what tech is being used where, and whether models, valuable IP and customer data are secure and compliant.
The power of “AI-first” infrastructure
A purpose-built, end-to-end, optimized AI environment, based in the cloud, can effectively address all three requirements, says Manuvir Das, Vice President of Enterprise Computing at NVIDIA.
Standardizing on clouds, tools and platforms such as NVIDIA AI Enterprise replaces the eclectic sprawl of diverse technologies across the organization with an optimized, end-to-end environment. All hardware and software networks are designed to work together. It’s analogous to an enterprise standardizing on VMware for virtualization, Oracle for database or Salesforce for CRM, Das explains.
Standardization removes the complexity of selecting, building and maintaining a tech stack, eliminating guesswork and the unpleasant surprises open source can bring. Major benefits include improved simplicity, efficiency and speedier development, operations, training, maintenance, support and growth. These platforms come backed by a dedicated partner with the expertise required to keep solutions tested, running and up to date.
“In all of these areas, teams don’t have to do all the groundwork themselves anymore,” Das explains. “A standardized platform allows them to get to productive work much more quickly. And once that work begins, it’s so much faster because it’s accelerated not just on the processor level, but across the whole acceleration chain, storage, networking and more.”
Simplifying cost control and governance
Today it’s possible to optimize infrastructure based on an enterprise’s workload — if you don’t need a behemoth capable of large inferencing, a standardized platform built for smaller footprints dramatically lowers the cost.
From there, cost control comes in several ways. First, IT takes back oversight on spending, with full visibility into who is making purchases and what they’re buying. Secondly, standardized environments bring economies of scale in purchasing and integration. Third, dedicated AI infrastructure accelerates processing of AI workloads. That means less time spent racking up a cloud bill for training, inference and scaling. That in turn, can free funds to invest in developing new AI use cases and unlocking new opportunities. It can also integrate a culture of AI innovation across a company, inviting more teams to conceptualize and kick off their own ideas.
“Every team that’s working on AI has gone through a struggle within the company to get funding to launch their projects,” Das says. “Once it’s standardized as a platform within that company, it makes it much easier for the next AI project to begin. And every team will see an opportunity to use AI to make their part of the business better.”
And for governance, a standardized AI cloud infrastructure offers accountability, with the ability to measure crucial metrics such as cost, value, auditability and regulatory compliance. Plus, the layers of security built into every aspect of purpose-built infrastructure offers a greater measure of defense against bad actors and keeps business-critical data private.
Making AI accessible across the organization
“For this next wave of technology and innovation, companies need to bet on an AI platform they can deliver across the company,” Das says. “A dedicated, standardized platform means no longer starting from scratch, putting AI into the hands of more of your people, doing more with smaller teams and lower costs. It can stop the chaos, reinvention of the wheel and projects withering away before they really start.”
To learn more about how dedicated AI infrastructure unlocks innovation across the enterprise, speeds up development and time to market, improves security and more, don’t miss this VB On Demand event.
Agenda
- Enabling orderly, fast, cost-effective development and deployment
- Focusing and freeing funds for ongoing innovation and value
- Ensuring accountability, measurability and transparency
- How infrastructure directly impacts the bottom line
Speakers
- Nidhi Chappell, General Manager, Azure HPC and AI, Microsoft
- Manuvir Das, Vice President of Enterprise Computing, NVIDIA
- Joe Maglitta, Senior Content Director & Editor, VentureBeat (Moderator)
VB Lab Insights content is created in collaboration with a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected]
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