How to Become a Full Stack AI Developer

In a previous issue, I introduced the four pillars of successful AI development: a clearly defined business case, radical end-user focus, a data-centric approach to model development, and appropriate operationalization.

I firmly believe that AI practitioners, including data scientists, should strive to be full-stack developers.

To me, being full-stack means covering the complete process of developing AI-powered applications, rather than simply knowing various technologies. While end-to-end is an alternative term, it doesn't make for a good newsletter name.

Being full-stack is especially important for practitioners in small AI teams, such as those in startups or enterprises just getting started in AI.

This may go against common practices in the tech industry, where professionals are often hired for specialized roles, such as ML researcher, ML engineer, data scientist, or AI product manager.

In this issue, I want to present the benefits of being a full-stack AI developer, the inherent trade-offs, and how to become one.

Let’s dive in.

Benefits of being a full-stack AI developer

In a brilliant post, Eugene Yan introduced the advantages of being an end-to-end data scientist. Here, I will keep it brief.

Increased context: With end-to-end knowledge, you will have a better understanding of the entire development process and the underlying business case. This leads to...

Decreased coordination overhead: You will require less syncing with your co-workers, giving you more time for deep work and quicker iteration.

Clear ownership and accountability: As the project owner, you will be responsible for the outcome. No one else can be blamed if something is not working.

Skill transferability: Building a skill set that can transfer well between organizations and clients will make you an effective individual contributor. It also prepares you to become a freelancer if that interests you.

Preparation for leadership roles: Understanding the end-to-end process of developing AI products is essential for leadership, whether you aim for tech lead or management roles.

Trade-offs you need to be aware of

This is all fun and games, but becoming full-stack inherently comes with some trade-offs.

Your skill set will be T-shaped, meaning that while you can develop basic skills for each of the four pillars, you won’t be an expert in all of them. As full-stack AI developers, we embrace breadth over depth of knowledge.

If you decide to pursue this path, there are a few truths that you must accept:

• Acknowledge that it's impossible to be an expert in everything.

• Realize that even specialists can't keep up with every new paper, given the rapid pace of developments in the field.

• Avoid getting sidetracked by the latest news and tools.

Instead, focus on building a strong foundation of skills in each of the four pillars of AI development. Then, go after the metagame.

Let’s go into more detail.

First, build the fundamentals

Mastering the fundamentals is key to success in any field. For example, NBA players spend hours every day working on their jump shots, while musicians practice their scales.

To become a full-stack AI developer, you need to develop basic skills for each of the four pillars. Here are some examples:

• For defining a clear business case around your AI project, you need to understand how businesses work, what the underlying value drivers of AI are, and how to conduct a feasibility study.

• Having an end-user focus requires you to know the fundamentals of prototyping and UX design.

• To develop ML models in a data-centric manner, you need to know how to handle and validate different forms of data and perform error analysis.

• Putting your model into production is only possible if you understand the basic building blocks of an AI-powered application.

I’m by no means an expert in any of these areas, but we will dive into each of them in upcoming issues, and I will point you to good resources that have helped me on my journey.

Then, go after the metagame

I first stumbled over this term in an article by Cedric Chin. He calls it “the game about the game”.

I know that’s abstract, but bear with me for a second.

In business, the metagame is about observing how the rules and best practices of an industry change over time, and acting accordingly to stay ahead.

In a fast-moving field like AI, playing the metagame means applying proven mental models to distill the signal from the noise.

In essence, this allows you to look beyond the hype and identify:

1. the sources of actual business value in new technologies.

2. the key trends that will shape the industry for years to come.

Let me give you an example of each.

• Generative AI is hyped right now. New research papers and tools pop up seemingly every hour, promising to make you more productive. However, all of the current tools revolve around two use cases: autocomplete for everything and answer engines. Now that you are aware of that you can ask yourself: where can I build around these use cases to create business value for me or my organization?

• Most research papers increase the state-of-the-art on some random benchmark by 0.1%. You probably will not derive business value from these incremental improvements. But every once in a while, trends pop up that will have a massive impact. The Transformer paper sparked an entire industry around NLP tooling, e.g., Huggingface. Currently, we’re shifting from a model-centric to a data-centric way of approaching AI problems. Being aware of these overarching trends allows you to strategically extend your skill set.

My goal with this newsletter is to share my journey of becoming a full-stack AI developer. Thus, upcoming issues will be related to both the fundamentals and the metagame of AI development.

That’s it for this week’s edition of the Full Stack AI newsletter.

See you next week!

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