管窥程序

Before we dive into the final chapter of this book, I’d like to address the serious matter of newcomers to AI sometimes experiencing imposter syndrome, where someone — regardless of their success in the field — wonders if they’re a fraud and really belong in the AI community. I want to make sure this doesn’t discourage you or anyone else from growing in AI.

The path to career success in AI is more complex than what I can cover in one short eBook. Hopefully the previous chapters will give you momentum to move forward.

In this chapter, I’d like to discuss some fine points of finding a job.

If you’re preparing to switch roles (say, taking a job as a machine learning engineer for the first time) or industries (say, working in an AI tech company for the first time), there’s a lot about your target job that you probably don’t know. A technique known as informational interviewing is a great way to learn.

Finding a job has a few predictable steps that include selecting the companies to which you want to apply, preparing for interviews, and finally picking a role and negotiating a salary and benefits. In this chapter, I’d like to focus on a framework that’s useful for many job seekers in AI, especially those who are entering AI from a different field.

Over the course of a career, you’re likely to work on projects in succession, each growing in scope and complexity. For example:

It goes without saying that we should only work on projects that are responsible, ethical, and beneficial to people. But those limits leave a large variety to choose from. In the previous chapter, I wrote about how to identify and scope AI projects. This chapter and the next have a slightly different emphasis: picking and executing projects with an eye toward career development.

One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. These next few chapters will discuss finding and working on projects so you can gain experience and build your portfolio.

How much math do you need to know to be a machine learning engineer?

In the previous chapter, I introduced three key steps for building a career in AI: Learning foundational technical skills, working in projects, and finding a job, all of which is supported by being part of a community. In the chapter, I’d like to dive more deeply into the first step: learning foundational skills.