Once you’ve reached the point where you want to deploy your machine learning models to production, you will eventually need to monitor operations and performance. You might also want to receive alerts in case of any unexpected behavior or inconsistencies with your model or your data quality. This is where you most likely start learning about various aspects of Machine Learning Operations (MLOps).
From an economic perspective, we see COVID-19 as the thing that brought global business to a standstill. But I think that in the longer term, we’ll see it as the catalyst for accelerated digital transformation. The effect of the virus on business and IT leaders has been to dramatically compress the strategic long view. The trends businesses saw coming over the next five to ten years, the developments that will drive growth opportunities or pose a disruptive threat ...
As colleges and universities work through their plans for a safe reopening this fall, they’re realizing that one of the most profound impacts of the COVID-19 pandemic has been the system shock it delivered to the planning process. Post-secondary institutions have always had to be masters of innovation.
You and your team have a lot of things begging for your attention. You’ve got multiple systems in place, and if anything goes wrong, the last thing you need is a storm of notifications coming at you from everywhere. To help you centralize your messaging and incident management, Uptrends continues to add integrations with tools that your team may already use. So, if you use Opsgenie, this new integration is for you.
Java is a well-established object-oriented programming language that epitomizes cross-platform software development and helped to popularize the “write once, run anywhere” (WORA) concept. Java runs on billions of devices worldwide and powers a huge range of important software, such as the popular Android operating system and Elasticsearch. In this tutorial, we will go over how to manage Java logs with the ELK Stack and Logz.io.
As a follow up to my previous “personal policy” blog I have exciting news: An improved CFEngine is available for Termux!
Mighty Keif the Kraken has reached across the interweb to join tentacles with his good friend Mona the Octocat once again. 🦑🤝🐙 But this time, it’s all about the issues. The GitKraken Git GUI now supports issue tracking integrations for GitHub and GitHub Enterprise! If you already use GitKraken to manage your GitHub repositories, optimize your workflow even further with new benefits offered in GitKraken Git GUI v7.3.
One thing we believe at Grafana Labs is that your data should be presented in a way that makes sense to you. Since the release of Grafana v7.0, we’ve seen a lot more developers taking advantage of the new plugins platform, which helps them build high-quality custom plugins faster than ever.
Higher resolution metrics are critical for monitoring dynamically changing environments and rapidly changing application metrics. Examples where high resolution metrics are critical include high volume e-commerce, live streaming, autoscaling bursty workloads on Kubernetes clusters, and more. Higher resolution custom, Prometheus, and agent metrics are now generally available, and can be written at a granularity of 10 seconds. Previously these metric types could only be written once every 60 seconds.
As a network of microservices changes and grows, the interactions between them can be difficult to manage and understand. That’s why it’s handy to have a service mesh as a separate infrastructure layer. A service mesh is an approach to solving microservices at scale. It handles routing and terminating traffic, monitoring and tracing, service delivery and routing, load balancing, circuit breaking and mutual authentication.
