The last week I attended to the QCon London 2020 Conference (thanks mimacom for the opportunity!). It was a cool experience where I had the chance to learn from others' experience on software engineering. These are the notes I made for the talks that caught my attention the most:

One of the goals I set for this year is to explore Machine Learning (ML), so after having done a couple of courses here and there, I decided to do a -rather simple- starting project, where I could deal with some of the basic stages of the ML: Get the data, prepare it, choose a model, train it, evaluate it, export it, and make the predictions available for use. For this first project, I chose:

As we saw in the first part of this series, it is essential to monitor the health of our microservices, and to improve the tool we developed which helps us get metrics and forward them to an Elasticsearch instance. In this chapter we will pursue the question of how can we improve the processing of the result of the metrics endpoint.

I found this experience really nice – to be able to get some insights on how people have solved big issues and how they got pwned. What is more, it was good to see that I am personally going in the right direction (or at least, let’s say, getting on the trends)

On the actual vibe of microservices using, it becomes more and more necessary to monitorize the health of our distributed systems. Here we will expose a few ways to do it, concentrating us in Spring boot applications. Hands on!