Operations | Monitoring | ITSM | DevOps | Cloud

We have been using Azure Functions for years. Being able to easily deploy and run code on both Azure App Services and real serverless has been a killer feature for all of our asynchronous jobs and services. Unfortunately, the logging approach provided as part of the default template is not ideal. In this post, I'll introduce you to the first steps we take in all of our existing and new function apps to improve logging. A quick note about the Azure Functions runtime.

Today, I’ll be covering troubleshooting Azure Virtual Desktop (AVD) issues. I’ll cover the common causes of problems beyond logon and how you can monitor and troubleshoot to identify the root-causes of issues and how to resolve them resolve them. For information on troubleshooting logon problems and slow logons, please see my previous article: Troubleshoot Slow Azure Virtual Desktop (AVD) Logons.

The Rancher by SUSE team has been dedicated to fortifying the Rancher platform to be the most interoperable and adaptable platform for our customers and the wider community over the past year. In late 2022, we introduced the ‘Extensions Catalog.’, and earlier this year at KubeCon Europe in Amsterdam, we revealed our Rancher UI Extensions framework. Continuing this momentum, we are thrilled to announce the release of Rancher 2.8 and Rancher Prime 2.0.

Elastic Observability customers saw 243% ROI and $1.2 million in savings over 3 years For government and education organizations around the world, facilitating an efficient, reliable customer experience is essential when providing critical services and building trust with stakeholders. As technology infrastructure expands and the IT landscape becomes a complex mix of private cloud, public cloud, and air-gapped environments, the ability to see across all systems and data is challenging yet critical.

For the past few months, we’ve been working closely with the LangChain team as they made progress on launching LangServe and LangChain Templates! LangChain Templates is a set of reference architectures to build production-ready generative AI applications. You can read more about the launch here.

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).