Operations | Monitoring | ITSM | DevOps | Cloud

October 2022

What's the hype about Machine Learning?

Can it help businesses? Machine learning is an inescapable buzzword for many in the operations sector. Even friends and colleagues tend to make us aware of a new ML tool that may or may not be useful. While there are many ML tools in the market, not all are suitable for every business. Some tools, when tested, struggle to solve basic, everyday use cases. Therefore, when evaluating ML tools, other deeper questions and issues do arise.

How Logz.io Uses Observability Tools for MLOps

Logz.io is one of Logz.io’s biggest customers. To handle the scale our customers demand, we must operate a high scale 24-7 environment with attention to performance and security. To accomplish this, we ingest large volumes of data into our service. As we continue to add new features and build out our new machine learning capabilities, we’ve incorporated new services and capabilities.

AIOps Provider ScienceLogic Acquires Machine Learning Analytics Provider Zebrium to Provide At-A-Glance Root Cause Visibility

Moving toward its goal of freeing up resources of enterprise IT teams and optimizing digital experiences, AIOps and hybrid-cloud IT management provider ScienceLogic has acquired machine learning analytics firm Zebrium to automatically find the root cause of complex, modern (i.e., containerized, cloud-native) application problems.

Kubeflow 1.6 on Kubernetes 1.23 and beyond

Kubeflow is an open-source MLOps platform that runs on top of Kubernetes. Kubeflow 1.6 was released September 7 2022 with Canonical’s official distribution, Charmed Kubeflow, following shortly after. It came with support for Kubernetes 1.22. However, the MLOps landscape evolves quickly and so does Charmed Kubeflow. As of today, Canonical supports the deployment of Charmed Kubeflow 1.6 on Charmed Kubernetes 1.23 and 1.24.