Kubeflow, the ML toolkit on K8s, now fits on your desktop and edge devices! 🚀 Kubeflow provides the cloud-native interface between Kubernetes and data science tools: libraries, frameworks, pipelines, and notebooks. > Read more about what is Kubeflow To make Kubeflow the standard cloud-native tool for MLOps within the AI landscape, the open-source community has accomplished the aggregation and integration of many projects on top of Kubernetes.

Logz.io has a comprehensive API that allows you to send and query logs or metrics easily. Now, that process is getting even easier for infrastructure monitoring. Logz.io has added support for the open source grafana API. In other words, you can use the OSS grafana’s integration within Logz.io.

With the promise of unprecedented potential, artificial intelligence (AI) and predictive analytics have permeated into every field of business. Due to their ability to help retail staff serve customers better, personalize video recommendations based on users’ preferences, reduce employee churn, and detect fraud and security threats, AI and predictive analysis are rapidly being adapted across industry verticals.

As data continues to explode across the enterprise, we are finding that it is becoming increasingly challenging for organizations to keep up. A recent Splunk report, "The Data Age is Here," found that 57% of companies interviewed expressed that the volume of data is growing faster than they can manage, with 47% bluntly saying they will fall behind when faced with rapid data volume growth.

As your tech stack increases, every new device (network devices, servers, applications) creates a large amount of distributed log data. This forms part of what is called “machine data”, which is growing 50x faster than traditional business data. In fact, everything in your stack is continuously writing new events to your log files, including error logs that contain a record of critical errors encountered by a running application, operating system, or server.

Managing large numbers of checks by hand quickly becomes cumbersome. Luckily, Checkly's REST API allows us to automate most of the repetitive steps. Building on that API, the Checkly Terraform Provider takes automation one step further, enabling users to specify their active monitoring setup as code. In this article, we will be building on top of John Arundel's great intro from a few months back and showing how to manage multiple checks using groups and shared code snippets.

Canonical, the publisher of Ubuntu, releases Charmed Kubeflow, a set of charm operators to deliver the 20+ applications that make up the latest version of Kubeflow, for easy consumption anywhere, from workstations to on-prem, public cloud, and edge. > Visit Charmed-kubeflow.io to learn more. Kubeflow provides the cloud-native interface between Kubernetes, the industry standard for software delivery and operations at scale, and data science tools: libraries, frameworks, pipelines, and notebooks.

The Internet of Things (IoT) is a term for the ever growing number of internet connected devices that fall beyond the realm of your typical laptop, desktop computer or smartphone. Many of us already own and use IoT devices on a daily basis, these could be anything from "smart" versions of appliances like refrigerators, thermostats and coffee machines through to your expected IoT devices such as Amazon’s Alexa & Google’s home speakers.

In May, we announced new updates across the Splunk Connected Experiences portfolio, including support for many popular mobile device management (MDM) providers. With support for MDM capabilities, our customers can securely deploy Splunk Mobile at scale. Today, we are excited to share that Splunk Mobile now supports another popular Mobile Device Management (MDM) provider: Microsoft Intune. You can manage Splunk Mobile on both iOS and Android devices through Microsoft Intune.