Operations | Monitoring | ITSM | DevOps | Cloud

GenAI is everywhere, and it’s changing how we approach technology. If you’ve ever wanted to dive into the world of large language models (LLMs) but felt intimidated, there’s good news! Hugging Face recently launched a self-paced course that’s perfect for beginners and more experienced enthusiasts alike. It’s hands-on, approachable, and designed to work on standard hardware thanks to the small footprint of the models.

Over £2 billion has been earmarked for the IT modernisation of the NHS. Let's explore some of what this means for IT teams and how Redgate can help. The NHS, England’s national health service, has always been a marvel – a complex, interwoven system delivering life-saving care to millions. But behind the clinical brilliance lies an equally intricate network of databases, servers, and IT systems that keep the whole operation humming.

If you are reading this, you likely understand how Machine Learning (ML) and Artificial Intelligence (AI) can improve your organization, from operational efficiency to boosting your bottom line. Yet, setting up and running a robust AI infrastructure, including a massive dataset for training and specialized tools, can be expensive. Amazon Bedrock wants to simplify this challenge, enabling you to build generative AI applications faster and easier. But is Amazon Bedrock pricing worth it?

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.

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.

Connecting Python projects to Oracle databases is an essential step for many workflows, but the process can feel unnecessarily tedious. Dependency-heavy setups, complex setup files like tnsnames.ora, and platform compatibility issues can make even straightforward projects frustratingly complex. To keep workflows running well, developers must discover a reliable and effective approach to connect Python to Oracle.