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The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

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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.

Building a one-stop Open Source Observability Platform | OpenObservability Podcast

Pranay, one of the the co-founders at SigNoz, was recently invited as a guest speaker by Jonah Kowall, CTO at Logz.io on his OpenObservability Podcast. In the podcast, Pranay talks about the mission behind SigNoz - unifying traces, metrics, and logs in a single platform and interface. He also shared anecdotes about the evolution of SigNoz since its inception, the community adoption, and its contribution to SigNoz.

How to add a Golden Signal to a service in Gremlin RM

In this video, we show you how to add a Golden Signal to a service. Gremlin uses your Golden Signals to ensure your services are still healthy and responsive during reliability tests. You can configure Golden Signals to use an existing monitor in your observability tools, such as Datadog, New Relic, or Prometheus. We recommend adding all four Golden Signals to each of your services to ensure comprehensive coverage.

Measuring Cloud Unit Costs for FinOps

Cloud adoption has been on an upward trajectory for over a decade with no signs of slowing down. As widescale migration becomes the norm, organizations are realizing cloud financial management — also referred to as FinOps — is critical to creating long term value in the cloud. Building a culture of financial discipline requires visibility and a strategy for measuring success along the way.

Feature Focus: August 2022

It’s already September! Time flies by when you’re getting things done, and we’ve been a busy bunch of bees here at Honeycomb. 🐝 We’re excited that we’ve gotten to share some of those changes with you already, like our relaunched interactive sandbox and the beta release of our OpenTelemetry log support and Go distribution, but that’s just the tip of the iceberg.

Tools for Time Series Data Science Problems with InfluxDB

This article was originally published in The New Stack and is reposted here with permission. You might need to perform anomaly detection or forecasting if you’re working with time-series data. The first step before working on your time series is finding the right data store. To effectively detect or forecast your data, you will require a data store that can handle a large volume of data at a high ingest rate. Therefore, you might want to look at using a purpose-built time-series database.

The Theme Park Workplace: A Modern Approach to IT Operations

IT teams in modern workplaces are no longer spending the bulk of their time troubleshooting and break/fixing issues. As in any service industry in the consumer world, IT service workers are now expected to deliver a great experience to their consumers – the employees. Managing the workplace has become much more like managing a theme park, where every aspect of its real estate should exhibit interest, joy, and fun; everything that makes up a great experience.

How to reduce MTTR with Grafana Loki and Grafana Tempo: Inside the Houzz observability renovation

Houzz is where millions of homeowners and home improvement professionals go to seek inspiration and supplies for their remodeling projects. But to continue as the leading platform for home remodeling and design, the Houzz tech stack needed a renovation of its own as the company scaled. In response, the Houzz team began by revamping their monoliths into microservices.