Canonical is happy to announce that Charmed Kubeflow 1.7 is now available in Beta. Kubeflow is a foundational part of the MLOps ecosystem that has been evolving over the years. With Charmed Kubeflow 1.7, users benefit from the ability to run serverless workloads and perform model inference regardless of the machine learning framework they use.
Today’s Cognitive Network Operations Center (Cognitive NOC) is a significant advancement that employs artificial Intelligence (AI) and machine learning (ML) to dramatically modernize and improve network management and operations. Working together, the NOC and IT Process Automation (ITPA) propel superior efficiency and effectiveness of network operations, minimize downtime, lower operational costs, and overcome additional challenges in optimizing network performance.
So far in this series, I’ve outlined how a scaling enterprise’s accumulation of data (data gravity) struggles against three consistent forces: cost, performance, and reliability. This struggle changes an enterprise; this is “digital transformation,” affecting everything from how business domains are represented in IT to software architectures, development and deployment models, and even personnel structures.
Google Cloud provides its own set of metrics for monitoring applications, services, and instances. There are a huge number of metrics – more than 1,500 different ones just for GCP monitoring! While this is great, dealing with such a number can also be overwhelming. Filtering, pulling, exploring, and storing the metrics that you really need can be an enormously time-consuming task, and a big challenge.
