Operations | Monitoring | ITSM | DevOps | Cloud

Mattermost v6.6 is generally available today and includes the following new features (see changelog for more details).

Confidence in the quality, robustness, and reliability of a product are among the most valuable qualities sought after by consumers as well as businesses. This confidence is built through the rigorous testing of a product. In software engineering, practices like extreme programming (XP) and test-driven development (TDD) champion the belief that automated testing should be used from the start of a project.

Today’s enterprises are struggling to cope with the complexities of their environments, technologies, and applications. On top of these challenges, they face faster release rates, and the need to always deliver the highest level of performance and availability to end-users, at the lowest possible cost.

By now you’ve probably gotten the message – multi-cloud DevOps (or a hybrid on-prem/cloud approach) is the future of development and deployment architectures. The benefits of this approach are pretty clear: future proofing your business, optimizing for performance and availability, avoiding vendor lock-in, leveraging the best tools/elements of each cloud provider, and more.

Modern DevOps teams that run dynamic, ephemeral environments (e.g., serverless) often struggle to keep up with the ever-increasing volume of logs, making it even more difficult to ensure that engineers can effectively troubleshoot incidents. During an incident, the trial-and-error process of finding and confirming which logs are relevant to your investigation can be time consuming and laborious. This results in employee frustration, degraded performance for customers, and lost revenue.

For many organisations, multi-cloud has become or is becoming inevitable. After all, it’s unlikely there is a single cloud out there that is able to support all your requirements. More than likely however, is the chance that your business is becoming multi-cloud by stealth. Organisations typically use several, to dozens, to hundreds of Software-as-a-Service (SaaS) products, as well as a handful of Infrastructure-as-a-Service (IaaS) hosting services, and development Platforms-as-a-Service (PaaS).

MLOps pipelines are a set of steps that automate the process of creating and maintaining AI/ML models. In other words, Data Scientists create multiple notebooks while building their experiments, and naturally the next step is a transition from experiments to production-ready code. The best way to do this is to build an effective MLOps pipeline. What’s the alternative, I hear you ask? Well, each time you want to create a model, you run your notebooks manually.