Ever been jolted awake by a midnight alarm because some server decided to take a sudden break? If you’ve been in IT operations, you know this isn’t just about fixing a problem; it’s about understanding and fixing it. Think of a favorite detective show, the detective is not just identifying the culprit, they are aiming to unravel the mystery “who done it?” and understand the motive.

In today’s world, with Large tech giants and businesses looking forward to moving toward serverless architecture, there has been a significant demand for scaling the applications. It’s therefore no surprise that millions of companies worldwide have adopted, or are planning on migrating to a Kubernetes and AWS Lambda solution to take their serverless applications to the next level.

Victor Sonck is a Developer Advocate for ClearML, an open source platform for Machine Learning Operations (MLOps). MLOps platforms facilitate the deployment and management of machine learning models in production. As most machine learning engineers can attest, ML model serving in production is hard. But one way to make it easier is to connect your model serving engine with the rest of your MLOps stack, and then use Grafana to monitor model predictions and speed.

Altice Portugal is a wholly owned subsidiary of Altice Group, a multinational cable and telecommunications company. They have a presence across Europe, including in Belgium, France, Luxembourg, Portugal, and Switzerland, as well as in the Dominican Republic, the French West Indies, and Israel. With annual revenues of more than $2.8 billion (2,629 million Euros), Altice Portugal is Portugal’s largest telecom company. Altice offers fixed, mobile, and satellite network services to consumers.

Microsoft Azure is one of the most comprehensive and broadly adopted cloud service providers in the industry, offering over 200 fully featured services from data centers globally. A wide spectrum of organizations across all verticals use Azure – to lower costs, become more agile and innovate faster. Tight integrations with the Microsoft ecosystem and product portfolio make Azure highly attractive to many.

A common question we get asked is “what client library should I use with InfluxDB 3.0?” This question isn’t as simple as it may seem. It can get confusing when deciding which client library to use while developing applications to write to and query from InfluxDB. There are numerous options to choose from and the answer may differ based on the following criteria: At first, this seems like an easy answer.

What does the Rasmussen model teach us about Site Reliability Engineering?