Xavier Geerinck

One of the ideas I have been playing around with the last couple of months is the combination of Digital Twins and Reinforcement Learning. This is an experimental idea where I would love to hear your opinions about it (feel free to comment below, send me an email or reach out to me on Social Media such as Twitter / LinkedIn), and that will be refined over the coming months.

Looking at the history in manufacturing, we saw that many manufacturing companies were deploying statistical models on-edge (e.g. based on parameters such as temperature, pressure and speed, do we send an alert or not), having the main impact on their production volume. If they can then squeeze out this extra bit of improvement, they would thus be able to optimize this process and have a growth in production volume (and net earnings accompanied with it).

Since I write a lot of Typescript projects, it's quite handy to have an explanation on how to set-up a typescript project.

In an earlier post, I went in more detail how you can utilize Dapr to create a Microservice that communicates over gRPC for quick Inter-Process Communication (IPC).

Due to the current COVID crisis I had some time to start revamping my blog, focusing on achieving features that were not possible before. All while still keeping the costs to a minimum (or even free). Here I would like to explain how I achieved just this, with the focus on adding the following features:

For most businesses out there it are hard times due to the recent crisis, causing a lot of pressure on optimizing OPEX costs as much as possible. That's why I would love to help all of you out there with some quick tips and tricks on how you can keep your spendings to a minimum on the Azure platform, or how you are able to gain quick benefits.

A couple of months ago it struck me that my personal knowledge in AI could be improved quite a bit. That's why I took the time to brush up on the different concepts ranging from data gathering until the evaluation of a deployed model.

The cloud war is heating up and one of the main challenges is the fear of lock-in created by these providers. Helping the raise of Kubernetes as a provider, since this allows customers to quickly move between one and the other (a deployment is a deployment, no matter the cloud you run it on.).

Creating a realtime application is quite trivial, you most of the time take in all the events and put a dedicated processor on there that will process the incoming events for you. But what if you would like to make things a bit more interesting and develop this entire path completely serverless without writing any code, but still ensuring a response of seconds rather than minutes?