Operations | Monitoring | ITSM | DevOps | Cloud

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.