Operations | Monitoring | ITSM | DevOps | Cloud

Running an Artificial Intelligence (AI) infrastructure on premise has major challenges like high capex and requires internal expertise. It can provide a lot of benefits for organisations that want to establish an AI strategy. The solution outlined in this post illustrates the power and the utility of the universal Operator Lifecycle Manager (OLM) using Juju, a universal OLM, to create and manage on premise AI infrastructure.

In this first edition of our Customer Spotlight series, we are joined by James Kahn and Ben Gittins of Idea 11. Idea 11 is an award-winning, full-stack AWS IT company that specializes in digital transformations, cloud infrastructure, software development and managed services.

One of the major concerns when moving to the cloud is how to approach AWS S3 security. Companies may have moved their workflows to Amazon, but are still cautious about moving their data warehouse. And that is totally understandable. We have all heard about data breaches in companies like Facebook, GoDaddy, and Pocket. It’s important that access to information is done properly, in a limited and controlled fashion, to avoid such breaches.

Running and troubleshooting production services requires deep visibility into your applications and infrastructure. Virtual machines running on Google Compute Engine (GCE) provide some system logs and metrics without any configuration required, but capturing application and advanced system data has required the installation of both a metrics agent and a logging agent.

In the 21st century, it’s quite easy to manipulate machines and computers. Our worries are no longer if something is doable, but if something can be perfected. Therefore, we mostly search for new ideas and ways to make our work impeccable. For example, if you’re using a particular software and you realize that the software is excellent, but it could be better in some ways that would allow you to work even faster, you’ll explore the alternatives.