Overall Strategy and Tactics as a Data Engineer â Machine Learning Engineer
NOTE:
First, this is not an Academic or Theoretical paper. Nor is this specifically about day-to-day efforts, per se, of a D.E. (Data Engineer) or ML (Machine Learning) Engineer. It is mainly about the path to Blasted Good Results & SUCCESS⌠using strategy and tactics.
For this little article, I also list 5 Caveats regarding these two (2) roles.
BUT. The "Caveats" are ânotâ restricted only to these roles. They can be applied to ANYTHING else - ANY role for ANY work out there
CAVEATS 1 â 5:
1) deep dive studying costs
2) deep dive study time
3) SIMPLE
4) Troubleshooting
5) Context
This paper is not about any kind of Diva effort such as someone uttering (or exhibiting) âIâm better than everyone elseâ.Â
Two of the primary areas are about Teamwork and Critical Thinking, in addition to the Caveats.
--- Teamwork is the primary here, with a good, successful end result for all, along with
--- Critical Thinking: you ABSOLUTELY must do a great deal in this area, in any effort you do â D.E., ML Engineering, Cloud Architect/Engineer â AS MUCH as possible
Of course, there are more strategic experts out there who does strategic thinking for a living, I am not anywhere near that level. I am only bringing it to the forefront for D.E.âs and ML Engineers to really ponder using strategy & tactics more on a daily basis....
