Machine Learning (ML) for Root Cause Analysis (RCA) is the state-of-the-art application of algorithms and statistical models to identify the underlying reasons for issues within a system or process. Rather than relying solely on human intervention or time-consuming manual investigations, ML automates and enhances the process of identifying the root cause.
Over the last few years we have slowly and methodically been building out the ML based capabilities of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting. Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a simple node level anomaly rate alert. note You can read a bit more about our ML journey in our ML related blog posts.
Monitoring and optimizing IT infrastructure, applications, and networks is crucial for businesses in today's digital landscape. It allows them to proactively identify issues, ensure optimal performance, and deliver a seamless user experience. However, traditional monitoring methods often fall short when it comes to handling the increasing complexity and scale of modern systems. That's where hosted graphite and machine learning come into play.
In its analysis of over 1,400 use cases from “Eye on Innovation” in Financial Services Awards, Gartner found that machine learning (ML) is the top technology used to empower innovations at financial services firms, with operational efficiency and cost optimisation as key intended business outcomes. ML is a branch of artificial intelligence (AI) that involves the development of algorithms and models capable of automatically learning and improving from data.
The TensorFlow framework, developed by Google, is one of the most important platforms for creating deep neural network research and for training machine learning algorithms. Generally, it runs on the server side using powerful GPUs, consuming power and a lot of memory.
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.
It’s time, take your things and let’s move on to more modern monitoring. Relax, I know how difficult the changes are for you, but if you were able to accept the arrival of DTT and the euro, you sure got this! But first let us do a little review: Traditional system monitoring solutions rely on polling different meters, such as the Simple Network Management Protocol (SNMP), to retrieve data and react to it.
