Most of my time, I spent trying to better understand how to "approximate" the Data Science/ML workloads to the Development stack of the company, so I spent a lot of time learning about containerization, and multiple ways to deploy those artifacts. On top of that, I started learning about the CI/CD stack, and introducing the CT (Continuous Training), by tracking metrics of the live models that were being served, and triggering data-drift alerts.
Most of my work was done using Python, and the FastAPI library, and the containerization was done mostly using Docker, but I had to gain an understanding of how to deploy it in cloud environments, at the time it was really valuable to learn Terraform to understand how to use Infrastructure as Code.
Most of the knowledge I was able to glue together came from webinars/lives/tutorials/books I found that already solved the same problems I was looking for.
If you have a solid Python foundation and already have some experience in Machine Learning in general, I would recommend the Practical MLOps book( https://www.oreilly.com/library/view/practical-mlops/9781098...), or even Introducing MLOps (https://www.oreilly.com/library/view/introducing-mlops/97814...) as starting points.
EDIT: Also, nothing beats a good project end-to-end, where you take a toy problem, and try to build an entire stack around it, from the training of simple ML models, tracking the models' versions, creating APIs to serve these models, monitoring, and so on.
This is what I think, I would like to know from you what I should prepare or focus on to get a good job.
Thanks in advance