Operations | Monitoring | ITSM | DevOps | Cloud

Machine Learning

Accelerating Innovation with MLOps Mastery

Machine Learning Operations (MLOps) is a methodology that combines machine learning (ML) with the principles of DevOps to streamline the development, deployment, and management of ML models. It addresses the unique challenges associated with operationalising ML, such as model versioning, reproducibility, and scalability.

Effective Observability for MLOps Pipelines at Scale with Rishit Dagli

Join Rishit Dagli as he explores effective observability for ML pipelines at scale. Learn about the critical differences between observability and monitoring in ML applications, common challenges like distribution shifts, and feedback loops. Rishit demonstrates practical methods for logging and interpreting various metrics to maintain model performance and reliability.

The strata of data: Accessing the gold in human information

I married into a family of geologists and rock and gem enthusiasts, and that bit of serendipity has added immensely to my life. Whenever we go on family hike excursions, I learn so much more about the landscape than I could have ever hoped to from my own educational path, and as a bonus my home gets adorned with tastefully and expertly chosen specimens from around the world.

AI Essentials - Your Gateway to Machine Learning with Kubeflow with Josh Mesout & Rishit Dagli

Join Josh Mesout and Rishit Dagli in our in-depth workshop on Kubeflow as a Service, aimed at demystifying the deployment of machine learning models using open-source tools. This Civo Navigate 24 session caters to participants of all skill levels in machine learning, Kubernetes, and ML infrastructure. It covers foundational aspects of Kubeflow, dives into advanced tools like Llama 2 and Kerve, and showcases how to use these models as cost-effective alternatives to services like ChatGPT.

How, and Why, We Applied Machine Learning to Cove Continuity, Part 2

If you haven’t already read part 1, click here to do that first. If you look at the screenshots, they’re actually quite simple to understand. Anyone can easily identify whether the OS booted successfully at first glance. Look at the following examples and you’ll see what I mean : So, rather than the existing deterministic method, which relied on indirect evidence, we opted to use machine learning and neural networks to analyze and classify screenshots like a human being.

Ways to Build Cybersecurity Resilience: Defending Against New Threats

In today's digital age, where cyber threats loom larger and more complex than ever, building cybersecurity resilience isn't just advisable-it's imperative. Each day, new vulnerabilities are discovered and exploited by cybercriminals who are becoming increasingly sophisticated in their methods. This reality makes it crucial for both individuals and organizations to fortify their cyber defenses to protect sensitive data and maintain business continuity.

How, and why, we applied machine learning to Cove Continuity, part 1

Over the next three blogs, I want to explain how we used machine learning to increase Cove Continuity boot-check accuracy to 99%. Cove Continuity offers the ability to restore source (protected) servers/workstations to virtual machines (VMs) in Hyper-V, ESXi, or Azure. After a VM is restored, Cove performs a boot-check test to prove that the system was properly restored.

Harnessing Technology for Seamless Mortgage Lender Discovery

In an era where digital solutions are at the forefront of transforming industries, the mortgage sector stands as a prime example of this revolutionary change. The complexity of selecting the perfect mortgage lender can overwhelm potential homebuyers, yet, technology simplifies this process, offering streamlined, efficient methods for comparison and selection.