ADSwR
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EdwinTh
"Agile Machine Learning with R - A workflow" is an opinionated take on how to do a data science project in R
Insightful blog by Neal Lathia about extending rule-based systems with ML instead of replacing it totally. Could fit in with the MVP + iterate narrative. https://nlathia.github.io/2020/10/ML-and-rule-engines.html
The 2nd part of the book might be a little too much in depth and prescriptive. In the upcoming version I'd rather focus even more on workflow.
As your work progresses your project becomes more complex. It gets more and more challenging to attain high quality. Start with high quality from the start, not a point you...
Adding a chapter for Product thinking. We might tend to focus on the data science task such as an algorithm or a statistical model, but this only a part of...
_Our highest priority is to satisfy the customer through early and continuous delivery of valuable software._ Now it is quite elaborate and it breaks the rhythm of the twelve points....
I think it is a bit too broad currently, it might improve if we split off the task part to a separate chapter.
David Robinson is an avid proponent of getting things in the open. This is very similar to getting things into production soon, even if this is internal for company.
Sit down and think what you will be doing next and actually doing it: * clear definition of done * leave trace of thought process * higher quality work *...
Discuss in more detail that this is not cutting corners in software quality, but making a selection in product scope.
I enjoyed reading you book. Thank you! I noticed that chapter 7 was ordered differently than the others. Instead of 7.0.1/7.0.2/7.0.3/7.1, shouldn't it be 7.1/7.2/7.3/7.4? Thanks a lot.
