In the first part, I described the context of how academic and industrial problem solving differ. The primary differences are the timeline of the solution and the complexity. When you prepare to solve a problem that will be part of a product, you can expect that the solution will be with you for an extended period. You will also have external performance metrics which you must surpass otherwise it will be deemed unfeasible. These conditions will create challenges but also opportunities, and given the longevity of the product, there will be time for more involved solutions.
@Laszlo Sragner Enjoying this series of articles very much from a PM perspective, however do you think you can post more on the technical side of ML products? e.g. how to set up and curate DS/ML repos, packaging models, etc... would love some of your opinion on best practices on that side
@Laszlo Sragner Enjoying this series of articles very much from a PM perspective, however do you think you can post more on the technical side of ML products? e.g. how to set up and curate DS/ML repos, packaging models, etc... would love some of your opinion on best practices on that side