Operations | Monitoring | ITSM | DevOps | Cloud

Check out Ken Ahrens and Scott Moore as they discuss some blockers of developer productivity when building in Kubernetes, and how removing environment and data challenges can reduce toil and frustration! You can catch the full podcast on Scott’s page here: Scott Moore: Hey everybody out there in internet meme land. It’s time to hide your kids and hide your wife because it’s time for the SMC Journal podcast. Some of you will get that joke. Others will not.

I've been in the observability market long before it even had that name. Over the years, observability has undergone a significant transformation. As someone who has witnessed these changes firsthand, I can attest to the dynamic nature of this field. In the early days, it was largely about basic monitoring: tracking system metrics, lots of logs, and simple alerts.

We recently released two new Raygun API endpoints that expose time series aggregations for particular metrics. They enable you to consolidate your company’s metrics into an external dashboard (such as Grafana, Tableau, or PowerBI), correlate errors and page performance with data points that Raygun cannot see, or even perform advanced data analysis.

To meet customer needs for scale, speed, and precision, IBM partners with Elasticsearch to deliver retrieval augmented generation (RAG) capabilities that can be seamlessly integrated into the IBM watsonx Assistant’s new Conversational Search feature. Customers using IBM watsonx Assistant and watsonx Orchestrate can now build conversational AI assistants grounded on their company data with comprehensive search capabilities with RAG.

When looking back at the role artificial intelligence (AI) has played in revolutionizing different industries that would typically require human intelligence, it is important to consider the next steps in this journey and how it is starting to evolve. With the growth of the industry, the volume and complexity of data are becoming unmanageable for pre-existing AI models.

In the world of IoT, ensuring the reliability, efficiency, and security of connected devices is critical. As IoT devices generate massive amounts of data, detecting anomalies becomes increasingly important. Anomaly detection helps identify potential issues before they escalate, providing businesses with valuable insights and the ability to improve operational efficiency if used correctly. In this article, you will learn about some potential use cases for anomaly detection across different industries.