Machine learning is a powerful tool that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Two fundamental approaches to machine learning are supervised and unsupervised learning. In this blog post, we'll explore the key differences between these two approaches, along with examples of their applications.

Looking for a way to concisely set a variable conditionally? Have you heard of ifelse()? In CFEngine, traditionally class expressions are used to constrain promises to different contexts. Setting a variable to different values based on context might look like this: /tmp/feature-friday-1.cf command output That’s great, lots of flexibility, but with an increasing number of options the policy can get quite long and it’s easier for a human interpreter to lose track of the context.

Cloud-native monitoring is all about keeping an eye on how well your cloud-based apps and services are doing. It's like having a health check-up for your digital operations, making sure everything runs smoothly and efficiently across distributed cloud systems.

Cloud computing has transformed the landscape of IT and business over the past several years, offering unparalleled speed, scalability, flexibility, and cost-efficiency to organizations of all sizes. However, amidst the widespread adoption of cloud technologies, there's a noteworthy phenomenon that's also gaining attention and traction: cloud migrations and repatriations.

I was out there in sunny Austin this February, speaking at Civo Navigate 2024. The event was jam packed with amazing talks, and it was great meeting so many people with long and fascinating careers in engineering and Site Reliability. I had the privilege of meeting Bob Lee, who currently leads DevOps at Twingate — a cloud-based service that provides secured remote access, and poised to replace VPNs.

ELK, which stands for Elasticsearch, Logstash, and Kibana, is a popular open-source software stack used for log and data analytics. The ELK stack offers a powerful platform for centralized logging, monitoring, and analytics and excels at presenting data with dashboards for effective analysis.

One of the fundamental promises of the cloud, when organizations embrace it, is significant cost savings compared to its on-premises costs. However, organizations to realize savings is required to proactively plan and monitor the application’s cost at a granular level. Azure cost anomaly detection involves promptly identifying, rectifying, and analysing unexpected Azure cost events to minimize their impact on the business.

As software systems become more complex, navigating their inner workings has become increasingly difficult due to the evolution of more advanced architectures. While distributed systems, such as microservices and cloud-native architectures, offer benefits in scalability and agility, they also make it more challenging to pinpoint and resolve system issues. Traditional methods for tracking errors are often insufficient in these multifaceted environments.