I'm a Computer Science student with dreams of becoming an AI Engineer, and I've noticed that many job listings emphasize the need for AWS skills. I'm already familiar with AI theory and modeling, but I want to connect that knowledge with practical experience in cloud-based production. Ideally, I'm looking to create a structured plan to master the AWS tools and services that are particularly relevant for AI and machine learning applications.
1 Answer
It's kind of like trying to learn how to build a space shuttle just to make model rockets. The AI Engineer title is still pretty vague. You're likely to be dealing with data science or engineering roles, so you might need to know a lot about various aspects like networking or operating systems, depending on the job. You could choose to learn everything, or focus more on specific areas to get a clearer direction.
That's a valid comparison! Being overwhelmed by AWS jargon makes sense, especially in job postings. My aim is to understand the core skills needed to deploy models without becoming a full-blown Cloud Architect. Thanks for guiding me to focus on data instead of drowning in all that cloud infrastructure.