Operations | Monitoring | ITSM | DevOps | Cloud

When working with ML models, it’s fair to say that a model is only as good as the data it was trained on. Training and testing models on quality datasets of an appropriate size is essential for model performance. Because of the intricate link between a model and the data it was trained on, it’s also important to be able to store datasets and versioned models together.

There’s no shortage of new developer tools promising to reduce toil and improve productivity. But investing in the latest technology can require a leap of faith organizations can no longer afford to make. Leaders need assurance that the investment they’re making will pay off, quickly.

Cloud storage solutions can provide public sector organisations with a high degree of flexibility when it comes to their storage needs, either public cloud based, or in their own private clouds. In our previous blog post we looked at the economic differences between these two approaches. In this blog we will explore some of the security best practices when using cloud storage, so that you can ensure that sensitive data remains securely stored and compliance objectives are met.

Traditional monitoring has become insufficient for managing complex systems. Modern infrastructures consist of numerous interconnected services, and simply monitoring individual metrics and logs fails to provide a comprehensive view. This is where observability becomes crucial.

Encryption is a fundamental part of nearly every modern application, whether you’re storing data, sending data to customers, or sharing data between backend services. Most organizations have a data encryption strategy, and nearly every web page is using HTTPS, thanks to initiatives like Let’s Encrypt. But setting up encryption isn’t a one-time initiative. Over time, the certificates backing modern encryption expire and need to be replaced.