Operations | Monitoring | ITSM | DevOps | Cloud

Learn how to measure and improve developer productivity with actionable tips, key metrics, and strategies for long-term success in software development.

Technology is advancing rapidly, and with it comes a growing demand for powerful computers, especially in fields such as machine learning (ML), artificial intelligence (AI), and high-performance computing. As these areas develop, the size and complexity of the data they handle also increases. This surge in computing power requirements necessitates new methods for processing large amounts of data efficiently, without sacrificing accuracy or speed.

The EU NIS2 directive, which calls for strengthening cybersecurity across the European Union, is now active in all member states. Join me for this 3-part blog post series in which I’ll explain what it is, help you understand if it is applicable to your company and how you can become NIS2 compliant.

The synergy between DevOps and MLOps is more crucial now than ever. However, merging these two paradigms into a coherent software supply chain poses a unique set of challenges that can leave teams feeling overwhelmed. From the complexities of managing model dependencies to adapting conventional CI/CD tools for advanced machine learning (ML) workflows, the path to integration isn’t without its twists and turns.

For financial institutions across the EU, and for organisations that do business with these institutions, Digital Operational Resilience Act (DORA) compliance is no longer a “nice to have” — it’s mandatory. No matter how you’re currently meeting and maintaining compliance with the DORA regulation, automation can improve efficiency and effectiveness of the controls you use to stay DORA-compliant.

Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.