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This post argues that ML is experimental in nature which makes it harder to plan for. That’s true of any R&D project though! Academia is typically not run on ka

This post argues that ML is experimental in nature which makes it harder to plan for. That’s true of any R&D project though! Academia is typically not run on kanban boards, for example.

ska 6y ago
Neither are early stage R&D projects in industry. It's a fallacy to believe that there is a bright line distinction though, and that no engineering practices or tools will improve your research productivity. Traditionally e.g. academic labs traditionally eschew all such approaches not because they wouldn't benefit, but because they don't understand what they are missing and have little incentive to learn, and few opportunities for it.
- lumost 6y ago
  Most processes have benefit proportional to the number of participants. As an individual little process is required, and spending time maintaining the process is extraneous overhead. Academics tend to want maximal freedom ( in exchange for minimal compensation ) and adding process that constrains their work is unlikely to be productive. Most ML projects in industry are leveraging understood techniques in novel ways to build real customer facing products within reasonable investment timelines. To say that one can't provide any scope or estimates on these seems weak.
  - ska 6y ago
    Fair point, but it's not so clear cut. A lot of process is about effective communication over largish groups and/or over largish times. Neither of these really apply in academic research work most of the time. On the other hand, there are practices that can save you a ton of time even if you are working alone. In an academic setting this can often mean you just learn the ineffectual techniques of whomever came before you. In this case a small amount of education and change of practice (i.e. process) absolutely can dramatically improve overall lab productiveness. Industry isn't immune at all - especially when treating your "R&D" group quite separately from your "production engineering". Another thing to remember is that there is no such thing as no process; you have one even if you don't understand it. It can be lightweight and almost invariably you can improve it (thought that's not always worth the time spent)

This post argues that ML is experimental in nature which makes it harder to plan for. That’s true of any R&D project though! Academia is typically not run on kanban boards, for example.

ska 6y ago

Neither are early stage R&D projects in industry. It's a fallacy to believe that there is a bright line distinction though, and that no engineering practices or tools will improve your research productivity. Traditionally e.g. academic labs traditionally eschew all such approaches not because they wouldn't benefit, but because they don't understand what they are missing and have little incentive to learn, and few opportunities for it.

lumost 6y ago
Most processes have benefit proportional to the number of participants. As an individual little process is required, and spending time maintaining the process is extraneous overhead. Academics tend to want maximal freedom ( in exchange for minimal compensation ) and adding process that constrains their work is unlikely to be productive. Most ML projects in industry are leveraging understood techniques in novel ways to build real customer facing products within reasonable investment timelines. To say that one can't provide any scope or estimates on these seems weak.
- ska 6y ago
  Fair point, but it's not so clear cut. A lot of process is about effective communication over largish groups and/or over largish times. Neither of these really apply in academic research work most of the time. On the other hand, there are practices that can save you a ton of time even if you are working alone. In an academic setting this can often mean you just learn the ineffectual techniques of whomever came before you. In this case a small amount of education and change of practice (i.e. process) absolutely can dramatically improve overall lab productiveness. Industry isn't immune at all - especially when treating your "R&D" group quite separately from your "production engineering". Another thing to remember is that there is no such thing as no process; you have one even if you don't understand it. It can be lightweight and almost invariably you can improve it (thought that's not always worth the time spent)