Operations | Monitoring | ITSM | DevOps | Cloud

A headline in a recent Register article jumped off my screen with the claim: “No, Kubernetes doesn’t make applications portable, say analysts. Good luck avoiding lock-in, too.” Well, that certainly got my attention…for a couple of reasons. First, the emphasis on an absolute claim was quite literally shouting at me. In my experience, absolutes are rare occurrences in software engineering. Second, it was nearly impossible to imagine what evidence this conclusion was based on.

So that’s the summer gone (hopefully, that heat was awful). Or winter if that’s where you are. Seasons change and so does the state of robotics. Fortunately, that’s what we’re here for. Before we get into it, as ever, If you’re working on any robotics projects that you’d like us to talk about, be sure to get in touch.

More and more companies are beginning to turn to DevOps and the cloud as a way to improve their software teams. Whilst it used to be that development and operations were seen as separate, that view has now changed. Linking the two leads to better communication, faster development times, and the ability to stay on top of things.

The cloud gets all the press today, and while organizations are moving more and more of their applications and associated infrastructure into the cloud, there is still a lot of “down below” on-premises. A recent cloud computing survey from IDG shows that a clear majority of companies plan to use cloud services for over half of their infrastructure and applications.

So what exactly is observability? Is it just a new-fangled term for 'monitoring'? Well, no. Observability goes further than mere monitoring. Observability involves the combination of 3 pillars – Metrics, Logs, and Tracing – to give a much more in-depth view of what your application is doing. Observability offers proactive insights into how your application and/or infrastructure are likely to behave, whereas monitoring is only reactive in nature.

If you’re here, it means that your application is a hit, coming through a long way of development and deployments. Your application is finally in a stage where you or your team need to set up more servers than you can handle manually, and you have to provision them fast. There’s also the need to make sure that all of them have the same configuration, packages, and versions in order for your application to have the same behavior in all of them.

Keeping track of what’s going on in Kubernetes isn’t easy. It’s an environment where things move quickly, individual containers come and go, and a large number of independent processes involving separate users may all be happening at the same time. Container-based systems are by their nature optimized for rapid, efficient response to a heavy load of requests from multiple users in a highly abstracted environment and not for high-visibility, real-time monitoring.

We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

Tigera serves the networking and policy enforcement needs of more than 150,000 Kubernetes clusters across the globe and supports two product lines: open source Calico, and Calico Enterprise. Our development team is constantly running smoke, system, unit, and functional verification tests, as well as all our E2Es for these products. Our CI pipelines form an extremely important aspect of the overall IT infrastructure and enable us to test our products and catch bugs before release.