Operations | Monitoring | ITSM | DevOps | Cloud

7 Critical Considerations for Evaluating Infrastructure Monitoring Platforms

I remember how excited I was to build my first Network Operations Center (NOC). It was a new idea at the time (yes, I know I’m dating myself), and boy, did we feel like we were cutting edge. The mere idea that we needed a place and a set of tools to monitor our entire infrastructure (because it’s never really been about just the network) was a big transition at the time. How things have changed.

Dashboard Design: Getting Started With Best Practices (Part 1)

Every day, dashboards are viewed more than 500,000 times at Splunk. They’re what make the sea of data intelligible and help tell a story when working with a team. However, constant net-new dashboard creation is not necessarily a value-add activity — it’s a workflow to rapidly turn data into doing.

7 Common Kubernetes Pitfalls

Kubernetes is the industry's most popular open-source platform for container orchestration. It helps you automate many tasks related to container management. Companies use it to solve their problems related to deployment, scalability, testing, management, etc. However, Kubernetes is complex and requires a steep learning curve. In this article, we will go through some common Kubernetes pitfalls most companies fall to.

How to Run Better Reports in Your Business ... and Why You Need To

Whether you own a million dollar company or run a small business at home, you're going to have to learn how to crack the code of creating efficient business reports that are comprehensive, factual, easy to grasp and reliable. In short, your business report is going to be a deal-breaker in your decision-making, since almost everything on your roadmap depends on the report you come up with. That's why it's important that you make no mistakes here, and try to run a report that doesn't miss any vital information. Here are some tips which should help you create a better report this year, with almost no stress or strain.
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How Is Machine Learning Used In AIOps?

When we think of computers, we typically think in terms of exactness. For example, if we ask a computer to do a numeric calculation and it gives us a result, we are 100% sure that the result is correct. And if we write an algorithm and it gives an incorrect result, we know we have coded improperly and it needs to be corrected. This exactness however, is not the case when dealing with Machine Learning. As a matter of fact, it is par for the course, that Machine Learning will be incorrect a percentage of the time.