Operations | Monitoring | ITSM | DevOps | Cloud

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

Technology juggernauts–despite their larger staffs and budgets–still face the “cognitive load” for DevOps that many organizations deal with day-to-day. That’s what led Spotify to build Backstage, which supports DevOps and platform engineering practices for the creation of developer portals.

As you know, having reliable checks is a cornerstone of synthetic monitoring. We don’t want false alarms, or worse, checks succeeding when things aren’t working. But sometimes, problems can be hard to identify because they only happen intermittently, or in certain situations. Similarly, monitoring results can be skewed by infrastructure issues, or network errors on the monitoring provider end, causing false alarms when there is actually no problem with the product.

Following our well-received presentations with Walgreens’ Andy Kettlewell at this year’s NRF and RILA LINK shows, we were fortunate to present another testimonial breakout session—this time from a CPG perspective—at Gartner’s annual Supply Chain Symposium/Xpo in Orlando. One of the things I’m especially proud of since the beginning of antuit.ai is the number of customers who’ve been eager to come forward and share their positive experiences with us.

The panel discussion “From Machine Data to Business Insights, Building the Foundations of Industrial Analytics” discussed modern methods and benefits of deriving insights from machine data. InfluxDB Developer Advocate Jay Clifford explained the trend now is to “allow the builders to bring the Lego blocks and build them together how they see fit.

Generative Artificial Intelligence (AI) is commanding conversations these days, a never-before-seen system that’s captured many millions of users since its debut in November 2022. A machine learning innovation that creates content of all kinds (and that’s just the beginning), generative AI also comes up with new product designs and optimizes business processes. We have only begun to exploit and understand this disruption.

Selecting a build system is an essential decision when creating a project. Changing is always painful, especially in a mature repository. Therefore the choice should be made carefully. With this article, I will try to describe a few advantages of what Bazel can provide in the context of an embedded repository and show how to set up a build environment with a cross compiler from scratch.