A lack of alignment between ML projects and the business can hobble efforts to scale the technology, said Will Kelly (@willkelly), a technical writer. “Data scientists and cloud engineers can overcome such challenges by taking a ‘land and expand’ approach,” Kelly said. “Such an approach leverages DevOps and cloud analytics, starting with small iterative pilot projects focused on business unit problems, then working up to enterprise-level production projects.”
“IT needs to realize there’s wisdom in age and wisdom in youth regarding technology,” said Will Kelly, technical marketing manager for a container security startup (@willkelly). “They shouldn’t trip themselves up on stereotypes and innuendos about generational differences.”
This sentiment is echoed by Will Kelly, technical marketing manager for a container security startup (@willkelly): “Do your homework so you can cut through the marketing hype and educate your stakeholders about edge computing in business terms.”That “homework” includes determining the right use cases and making sure your infrastructure and systems can support edge applications and workloads.
“Consider the team that was in the office pre-pandemic, then went remote, and are now contemplating a hybrid work model. They can use a no-code platform to improve their processes and workflows in real time at each stage using unfiltered feedback from their team members.” — Will Kelly (@willkelly), technical marketing manager for a container security startup
I’ve often said that one of the first casualties of my paid writing work are my personal writing projects like this blog you’re reading. Today, I added links to my recently published work over on SearchITOperations.com, SearchCloudComputing.com, and opensource.com