How to Plan for an AI Deployment
Some agencies are pushing ahead on AI deployments. For example, the U.S. Patent and Trademark Office recently inked a $50 million contract to use machine learning, natural language processing and AI technologies to improve intellectual property registration.
The road to issuing an AI contract can be long but should be followed deliberately to get the most value out of the investment. At the outset, IT leaders need to clearly identify what their objectives are and think through if or how AI technologies can help.
They can work with trusted partners such as CDW to whiteboard out those ideas, and can then get connected to industry leaders such as Microsoft, Google and IBM. Those discussions can then lead to a pilot program and, potentially, a larger AI deployment if the trial run proves successful.
AI is so buzzy and exciting that IT leaders may be tempted to invest in such solutions without thinking through what their intended goals are, or if AI is truly necessary. In some cases, it will not be necessary — or will only be necessary for a few applications or uses. That is why it is critical to talk through objectives with intelligent partners to develop a truly strategic plan.
What Are the Enabling Technologies for AI?
Once it has been determined that AI is the appropriate solution, it is important for agencies to invest in the necessary back-end technologies to support such deployments.
Some of those solutions are supercomputers, such as the NVIDIA DGX A100, which the Energy Department’s Argonne National Laboratory is using to “help researchers explore treatments and vaccines and study the spread of the virus, enabling scientists to do years’ worth of AI-accelerated work in months or days,” Rick Stevens, an associate lab director at Argonne, says in a statement.
However, there is a wide array of supporting and interlocking technologies needed to make AI a reality and effectively run AI software. They include “data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming,” notes a Cornell University research paper.
The kind of hardware needed to support such solutions doesn’t have to be a supercomputer. It could be a single Dell EMC PowerEdge R740xd master/login node and 16 Dell EMC PowerEdge C6420 dense compute servers in four C6000 chassis. Those nodes are then linked to a 1 Gigabit Ethernet connection for access to the outside network and the internet, and a 10Gb Ethernet connection for internal traffic and data movement. Other solutions include IBM Spectrum Storage, IBM Spectrum Computing and IBM enterprise AI servers.
IT leaders should focus on what their goals are and what the correct configuration of technology solutions is to perform the work needed to achieve those goals.
Such deployments can be expensive. It is wise for IT leaders to understand their requirements and their objectives before diving into AI.
AI is here to stay. But federal agencies need to approach the technology in a disciplined and logical manner.