Objective: ServiMon is designed to offer a scalable and intelligent pipeline for data collection and auditing to monitor distributed astronomical systems such as the ASTRI Mini-Array. The system enhances quality control, predictive maintenance, and real-time anomaly detection for telescope operations. Methods: ServiMon integrates cloud-native technologies-including Prometheus, Grafana, Cassandra, Kafka, and InfluxDB-for telemetry collection and processing. It employs machine learning algorithms, notably Isolation Forest, to detect anomalies in Cassandra performance metrics. Key indicators such as read/write latency, throughput, and memory usage are continuously monitored, stored as time-series data, and preprocessed for feature engineering. Anomalies detected by the model are logged in InfluxDB v2 and accessed via Flux for real-time monitoring and visualization. Results: AI-based anomaly detection increases system resilience by identifying performance degradation at an early stage, minimizing downtime, and optimizing telescope operations. Additionally, ServiMon supports astrostatistical analysis by correlating telemetry with observational data, thus enhancing scientific data quality. AI-generated alerts also improve real-time monitoring, enabling proactive system management. Conclusion: ServiMon's scalable framework proves effective for predictive maintenance and real-time monitoring of astronomical infrastructures. By leveraging cloud and edge computing, it is adaptable to future large-scale experiments, optimizing both performance and cost. The combination of machine learning and big data analytics makes ServiMon a robust and flexible solution for modern and next-generation observational astronomy.

These are heady times for InfluxDB, which is the world's most popular time-series database, which has been the fastest growing category of databases the past two years, per DB-Engines.com. But when Paul Dix and his partner founded it a decade ago, the company behind the time-series database and the product itself and looked much different. In fact, InfluxDB went through several transformations to get to where it is today, mirroring the evolution of the time-series database category. And more change appears on the horizon. Dix and Todd Persen co-founded Errplane, the predecessor to InfluxData, back in June 2012 with the idea of building a SaaS metrics and monitoring platform, à la Datadog or New Relic.

In this webinar, Sébastien Leger from Loud ML will share with you the power of using unsupervised learning frameworks to gain deep insights into your InfluxData time series data (application and performance metrics, network flows, and financial or transactional data). He will then show you how to configure, model, and dig into the modeled times series data using the Loud ML API and your existing InfluxDB databases. This will open the recording. Here is an unedited transcript of the webinar "How to Build Your First Predictive Model in Seconds with InfluxDB and Loud ML" This is provided for those who prefer to read than watch the webinar. Please note that the transcript is raw. We apologize for any transcribing errors. We have a really great webinar today. We actually always have a great webinar. But today, I'm really excited. We'll get started in just one minute. In the meantime, I'll just cover some housekeeping items. If you have any questions during the presentation, please feel free to type them in either the Q&A, or the Chat Panel. And if you really, really, really want to speak out your questions, just raise your hand and I can un-mute you and you can talk to Sebastian directly. In addition, as always, I will--this session's being recorded. After I do the edit, then I'll post it and you will get--usually you'll get the email first thing tomorrow morning. But I usually end up posting this in a couple of hours. So if you go back to the link, you'll see that the page actually will change from the registration page to the recording. So you'll be able to take a listen to it again. And also, we have trainings on Thursdays.

Time is a critical context for understanding how things function. It serves as the digital history for businesses. When you think about institutional knowledge, that's not just bound up in people. Data is part of that knowledge base as well. So, when companies can capture, store and analyze that data in an effective way, it produces better results.