I used to think my job as a developer was done once I trained and deployed the machine learning model. Little did I know that deployment is only the first step! Making sure my tech baby is doing fine in the real world is equally important. Fortunately, this can be done with machine learning monitoring. In this article, we’ll discuss what can go wrong with our machine-learning model after deployment and how to keep it in check.
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.
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.
