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• `experimental` is defined as 
  1. exploratory data analysis
  2. development in notebooks
  3. essentially ad-hoc choice of tools
  4. generally batch only, "one off", manual execution
  5. small data, manual sampling
  6. models are trained offline
  7. the end result being reports, diagrams, etc, 
• `production` = pretty much the opposite
  1. end result are enterprise data science applications 
  2. ran in production 
  3. with large, multi-dimensional data set`s that do not fit in RAM, logically infinite
  4. hence the algorithms / analysis must be incremental
  5. use of managed `data set`s : `data lake`s, `feature store`s
  6. models are trained onlineincrementally (_"training offline periodically and refreshed/deployed every few hours/days"_)
  7. with awareness of `concept drift`, `distribution drift`, `adversarial attacks` and able to adapt
  8. use complex orchestration between core analysis and  decision layer, model monitoring and other application logic and business processes, some involving human interactions

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