I agree that nothing bets practical projects, but in modern life, you need to learn a lot of background information before jumping into the real world. There are plenty of ML projects and examples that are equivalent to the ToDo (12-factor app) in complexity—single component, boundaries clearly defined. The next steps in the real world would be—here is a payment platform with 270+ services and components, how does your AI/ML component fit into it? Who do you talk to to figure out the business value of the AI/ML component (business analysis/domain driven design)? How do you talk to your creative colleagues who are responsible for user experience in a productive manner ( i.e. jobs to be done )?
I see this gap quite consistently and I am trying to address it on the technical side by building medium size AI/ML project with 3 pipelines http://thepattern.digital/ and I think modern ML/AL professionals need to know things above before jumping into any real-world project.
I agree that nothing bets practical projects, but in modern life, you need to learn a lot of background information before jumping into the real world. There are plenty of ML projects and examples that are equivalent to the ToDo (12-factor app) in complexity—single component, boundaries clearly defined. The next steps in the real world would be—here is a payment platform with 270+ services and components, how does your AI/ML component fit into it? Who do you talk to to figure out the business value of the AI/ML component (business analysis/domain driven design)? How do you talk to your creative colleagues who are responsible for user experience in a productive manner ( i.e. jobs to be done )?
I see this gap quite consistently and I am trying to address it on the technical side by building medium size AI/ML project with 3 pipelines http://thepattern.digital/ and I think modern ML/AL professionals need to know things above before jumping into any real-world project.