Preparing for Google Cloud Certification
Machine Learning Engineer

Depending on your job you may never have to deal with productizing the model. This can be the job of the MLOps guys. Your role may be purely to play with the data and the models.

Getting the models to production is a very-very different story. It very much "standard" software development and DevOps + most probably you'll need to learn some cloud services for your pipelines like Google Cloud.

Truth is - getting into ML is a long journey, most probably you'll have to keep learning new things from all the related areas. I wrote a full article on stepping into ML - https://vkontech.com/the-experienced-developer-stepping-into-machine-learning-why-and-how/

There are also good courses and specialization that focus on the "productionizing" side of the story - like this one:

https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate?

I know this sounds overwhelming. It shouldn't be! It's just about taking some small steps and giving yourself enough time. I've written quite extensively on this in the article from above.

https://www.coursera.org/professional-certificates/google-data-analytics

https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate

https://www.coursera.org/professional-certificates/ibm-data-science?

do these 3 for a LOT less money and you'll be far better off

then add

https://www.coursera.org/specializations/mathematics-for-data-science

https://www.coursera.org/specializations/mathematics-machine-learning