Titre article de blog
Sous titre
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Python is a dynamically-typed programming language. This means that the interpreter performs type checking only when the code runs and the variable type is allowed to change over its lifetime.
Although Python has always remained dynamically typed and was never intended to change this direction, type hints have been introduced since PEP 484 with the goal of bringing static type checking to the code analysis.
Python decorators are powerful tools that help you produce clean, reusable, and maintainable code.
I’ve long waited to learn about these abstractions and now that I’ve acquired a solid understanding, I’m writing this story as a practical guide to help you, too, grasp the concepts behind these objects.
No big intros or lengthy theoretical definitions today.
Moving from Jupyter notebooks to production is not that difficult after allIf you’re a data scientist, you probably spend a lot of time developing intricate Jupyter notebooks to perform data analysis, build complex training pipelines, or compute statistics.
Jupyter notebooks are great for this and allow us to prototype ideas in no time.
But, what happens once you’re done with this work and you’re satisfied with your saved ML models? 🤔
This is where you start to think about deploying them to production. Have you thought this through when you started working?
Probably not. And you’re not to blame as this is not a data scientist’s core expertise. (although the industry is currently moving towards this)
The goal of this article is showing a use case with a Beam pipeline containing a dead letter queue for errors applied with Asgarde library.
For more details about Asgarde library, you can check this article :
The goal of this article is to showing a complete example to trigger a Cloud Function with Event Arc, when a file is uploaded to Cloud Storage.
This Cloud Function will load this file to a BigQuery table with Event Driven pattern.
I have often seen this kind of question in the Google Cloud community on Stackoverflow, and through this article I wanted to help the community and show a real example while centralizing many useful links on this topic.
The goal of this article is to create Service Accounts with Custom Roles in Google Cloud using CI CD pipeline and Infrastructure As Code.
The use of custom roles is interesting because it allows us to follow the least privilege principle, which is to set only the necessary permissions.
The infrastructure we want to manage in Google Cloud is Service Accounts containing predefined and custom roles.
The tools chosen for this use case are :
Below you can see the use case diagram of this article :