What are your recent CIO hires? Knowing the answers and how to become a leader with those skills may be the ticket to the next stage of your career. More than half of the CIOs surveyed by IT analyst company Gartner in 2021 said they plan to increase the number of people to manage machine learning and AI initiatives.
The urgency is that AI workloads have traditionally required specialized IT infrastructure. In other words, until now. AI and data science workloads can now run on accelerated mainstream servers in enterprise data centers. This makes it easy for IT professionals to support these new applications.
About the author
Anne Hecht, Senior Director of Enterprise Solutions at NVIDIA.
Running AI in the enterprise has never been easier, but to understand the requirements of a project, IT needs to know the language spoken by AI professionals. Fortunately, the fastest way to learn AI terminology is also the fastest way to succeed in an AI project. Include your company’s data scientists when planning your enterprise AI infrastructure.
When it comes to AI, the most important users of a company are data scientists. They are serious AI experts and know that they need a powerful server with fast computing capabilities to get their work done on time. Data scientists process huge datasets and require a lot of computing power to iterate, improve, and modify AI models before moving them to production.
Placing data scientists in a table when creating an AI infrastructure plan helps define data scientist use cases and prepares your organization for long-term AI ambitions. It also provides live feedback to help eliminate options that don’t meet your requirements.
Go around the wagon
Involving a data science team in an AI plan may seem like a clear step on the road to success, but domains are new to many organizations and are not always the norm. ..
In fact, IT managers at major medical institutions recently mentioned how they were asked to invite three unknown people to a meeting. It turns out that three colleagues were on the data science team, so someone must have planned a guest list.
The IT team was able to incorporate their views and jump over dead-end ideas that didn’t match what the data science team needed to develop an AI project and put it into production. Working together meant that IT could focus on solving computing and infrastructure problems, and the data science team advised on application and workflow requirements.
“Shadow AI” poses a serious risk if the IT and data science teams are not connected. This happens when an AI developer finds a way to get the infrastructure they need outside of their organization’s IT infrastructure.
Many data scientists are willing to focus on their domain and leave the infrastructure to IT, but their work also requires powerful systems. In essence, data scientists tend to be creative problem solvers. They may handle the problem with their own hands if necessary.
Data scientists may utilize cloud computing outside the normal control of IT or run workloads on unsecured personal systems to find ways to get their work done efficiently. .. These workaround solutions can be easily avoided by adding both cost and risk to the enterprise and ensuring that IT works closely with AI professionals.
Easy enterprise AI in traditional infrastructure
Until very recently, AI infrastructure was most often isolated from the mainstream servers that IT uses to run most business applications. This has made AI and its infrastructure an area of expertise, but now it can run on industry-standard servers managed by corporate IT teams.
To easily deploy AI to traditional data center servers, look for dedicated enterprise AI software that can easily deploy these advanced workloads to a scalable, high-performance hybrid cloud. The right tools can integrate the AI frameworks and tools needed by data scientists and AI developers into the existing infrastructure ecosystem.
Become an AIIT expert
AI is a structural change in technology, similar to the change that organizations had to navigate when they first accessed the Internet. This new era of AI is creating demand for new skills, new software, and new systems. These create new opportunities for IT professionals.
AI workloads may be new to many companies, but it’s a great way for IT teams to develop new expertise that not only adds value to the organization, but also helps individual IT professionals grow their careers. Offers the opportunity.
By working with data science experts, IT can be successful for the first time and avoid potentially costly failures in unfulfilled purchases. In fact, looping data science professionals may be the basis for the next chapter of your IT career.
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