Computer vision is a sub-field of Artificial Intelligence that mainly train computers and systems to gain information from images and videos. Main computer vision types are object detection, image classification, facial recognition, image segmentation, feature matching, pattern identification, etc. Computer vision simplifies the work for humans such as self-driving cars, disease diagnosis, and many more.
Recently, I got into a scenario where I had to delete 104 million rows in a table which contained 293 million rows based on a condition. I was able to delete them in less than 30 minutes using a Java application and by making parallel calls to stored procedure. Let’s see how to do this. Stack I used: Java, AWS RDS with r6g.4xlarge size. You can apply the same approach to your choice of tech stack.
The Micro Frontend concept is highly inspired by microservices architecture. This will allow us to divide a Monolith Frontend into smaller pieces (micro-frontends) and implement, build, test, and deploy pieces of your frontend app independently of each other.
Migrating from a traditional test strategy to a fully automated test strategy with continuous deployment has been an exciting journey. We had to change our processes, technology stacks and status quo to make that happen. This article is a deep dive into those aspects followed by some lessons learned. Example git repo is linked at the bottom.
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