Operations | Monitoring | ITSM | DevOps | Cloud

Kubeflow is the open-source machine learning toolkit on top of Kubernetes. Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks. Read more about Kubeflow

The 2010 Stuxnet malicious software attack on a uranium enrichment plant in Iran had all the twists and turns of a spy thriller. The plant was air gapped (not connected to the internet) so it couldn’t be targeted directly by an outsider. Instead, the attackers infected five of the plant’s partner organizations, hoping that an engineer from one of them would unknowingly introduce the malware to the network via a thumb drive.

I’ve recently been talking to some of the users of our eG Enterprise monitoring solution and its AIOps-powered root cause analysis platform. Multiple users have mentioned how the usability of the solution has been enhanced considerably by the availability of rich featured mobile apps for iOS and Android platforms.

Gamers are not shy about reaching into their wallets for premium content and features. They also won’t hesitate to tap the uninstall button at the first sign of trouble. It’s not uncommon for a gamer to boot up a hotly anticipated new game or revisit an old favorite only to put it down days or weeks later. The culprit is often gaming monetization issues that get in the way of what would otherwise be a long-term rewarding gaming experience.

Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.

Globally the impact of AI is increasingly growing year on year in every industry sector. Opening new scopes in construction & architecture sector, the global AImarket is estimated to grow at a CAGR of 29.4% (around) from 2019 to 2026 and is expected to reach around US$ 2.1 Billion by 2026.

We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.

A data center typically employs between 30-200 people depending on the size and the intended project use. But only a fraction of those folks run the IT side. Besides swapping gear—the infamous “remote hands”—there isn’t much to do as long as there aren’t any outages, and rumor has it a data center is a nice place to sleep—it’s dark, cool, and the deep humming noise can have a pacifying impact on some.

Advances in artificial intelligence (AI) are among some of the most exciting technological revolutions of our time. An increasingly ubiquitous aspect of our digital environment, AI is changing how we interact with our devices and surf the internet. Most of us fall somewhere between the two poles of a utopian and a dystopian understanding of the new technology, seeing both its benefits for society and its inherent risks.

How do you choose an API style and API technology when you start a new project? Today, if you look at API technology research such as RapidAPI Developer Survey and Insights, you’ll probably conclude that REST is the dominant force in the API landscape. While REST is certainly well-known to most developers and used in a lot of production environments, it may not be the best fit for every scenario.