In my last few posts, I described a situation in which we wanted to roll out a new predictive model to the wet lab team to define the compound concentrations that would be used in upcoming experiments. When we left off, I suggested that there were two different approaches, depending on what you would do if the predictive model turns out to be junk. Last week I explored the first option - how we would proceed if a failed model meant we would scrap the project. This week, it’s option 2: When we’re planning to make this work one way or another, not matter what.
"rolling out" can be painful, so "seamless" is an excellent guideline. Seems hard to be be seamless on both ends tho. One of the most aggravating aspects for the end user, the "WET", is when the production system has deprecated and removed features/methods which were present in the prototype. Another is the effort to port over and populate the historical data from the prototype.
Dev sometimes will have two teams, a small for the prototype and a second much larger one for production. "deliberate empathy" (https://scalingbiotech.substack.com/p/empathy) could be useful here, too.
Some good points.
"rolling out" can be painful, so "seamless" is an excellent guideline. Seems hard to be be seamless on both ends tho. One of the most aggravating aspects for the end user, the "WET", is when the production system has deprecated and removed features/methods which were present in the prototype. Another is the effort to port over and populate the historical data from the prototype.
Dev sometimes will have two teams, a small for the prototype and a second much larger one for production. "deliberate empathy" (https://scalingbiotech.substack.com/p/empathy) could be useful here, too.