As an AI Generalist, you also need to understand Product Management and Project Management. You often find that managing workflows, products, and people is harder than building a standalone product. The biggest challenge as an AI Generalist is running projects in an extremely diverse ecosystem. You will have domain experts from different domains having different lingo; you will have AI experts who always love to talk in the language of AI; you will have data scientists who always love to dig into the data; and you will have diverse stakeholders who will overwhelm you with many requirements, especially conflicting requirements. Altogether, managing an AI Product is a complicated and complex task.
In this lesson, I will mainly discuss the areas an AI Generalist should focus on, based on my experience. The first thing is the scope. This matters because organisations are empowering everyone to use AI and build applications from itfrom it. In that case, the group of domain experts also have good exposure to AI. Now the question is: while developing the AI product, have you joined hands with the AI department, or have you hired an AI expert? Now, what are the scopes of the domain experts with AI expertise and a dedicated AI team/AI experts? As an AI generalist, one must define the scopes of every participant in the project.
The second most important thing is that the team speaks in different languages. The challenge is: as an AI Generalist, how can you enable cross-team collaboration? How do you help a domain expert give the right information to the AI team, even when the AI team isn’t sure what to ask for? On the other hand, how would you help the AI team ask the right questions of domain experts? Asking generic questions often doesn’t make sense when you are implementing a product concept.
The third most important thing is prioritising and removing waste, which we call Kaizens. For example, some AI models can be highly probabilistic. In that case, should we concentrate on developing such a challenging problem? Or should we focus on easier problems that solve a burning problem and deliver value immediately? As an AI Generalist, you must find the best-suited strategy. One small mistake can crash the entire product and prevent it from delivering its intended value.
Many stakeholders remain very sceptical about AI-based solutions. While empowering the development team, as an AI Generalist, you must enable them to validate AI solutions correctly. Validating AI models is far different from validating white-box models. For example, the automotive industry is building a very complex mathematical system. Although the system is complex, developers build each piece of the equation from principles used for years and proven by scientists/researchers. However, in an AI system, no one can fully explain every node in the Neural Network. It is just a network. So the developers must know how to validate every corner case of the AI-based solution.
In this lesson, I covered three crucial things that can keep an AI project from failing. I hope you enjoyed “The AI Generalist Program” and that it sparked ideas, roadmaps, and a mathematical way to analyse models.
With that, I’ll stop here. We are working to build an innovative learning framework that empowers people to solve problems, especially in a diverse country like India. We believe in three things in learning:
- Focus on the theory that makes you different.
- Solve real/industrial/practical problems that give you experience.
- Learn how to ship products to become a full-stack expert.
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