Operations | Monitoring | ITSM | DevOps | Cloud

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.

It’s time to stop proclaiming that “cloud native is the future”. Kubernetes has just celebrated its 10 year anniversary, and 76% of respondents to the latest CNCF Annual Survey reported that they have adopted cloud native technologies, like containers, for much or all of their production development and deployment. Cloud native isn’t the future – it’s here and now. Data-intensive workloads are no exception.

Following up on his discussion at IDPCON, Lou Bichard, Product Manager at Gitpod, revisits the topic of the developer inner loop. Lou Bichard|

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.

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?

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.