Over a quarter of the companies currently using AI claim that it’s directly responsible for 5% or more of their earnings. But the infrastructure needed to support AI and other resource-intensive initiatives, such as ML, is both complex and expensive, creating a major hurdle for organizations. Furthermore, finding the right experts isn’t a simple task.
Our new white paper, Maximize AI/ML Workflows with Powerful, Purpose-Built Infrastructure, provides you with a comprehensive understanding of how to overcome these challenges, moving beyond experimentation and targeted use cases to fully leverage the power of AI for your business.
Here’s an excerpt on implementing and managing containers at scale:
Another consideration for IT involves containers. Standing up AI workloads in containers provides maximum flexibility and efficiency for data scientists, but there are also other advantages. For example, many use cases — such as autonomous robots in factories and medical monitoring of patients' vital signs — cannot tolerate downtime. If IT were able to automate the movement of containers across environments, they could ensure workloads continue functioning in the face of both scheduled and unscheduled downtime. Additionally, containers can allow data scientists to take advantage of pre-trained models housed in containers to accelerate time-to-value.
This can be challenging because IT often lacks experience in managing containers. One could put IT through intensive training to gain those skills, but it’s also possible to leverage the VM management tools that they already use daily to manage containers. For most organizations, that will be the fastest, most efficient way to implement and manage containers at scale.
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