>Are you really offering an easy setup to the senior devops person? I'm sure they can figure things out.
I'm sure a mechanic would figure out their broken car; it just is not ideal to do so everyday on their way to their new job/course.
Plus, in this case, it is not only about mechanics (devops). The window jams and doesn't close when it's raining. The fuel the car can fire changes every week. The seats were salvaged from a 1920's car. Seat belts are too tight and thin. The pedals are slippery. The sequence to turn on the car is in their colleague's head, and he's often absent. The tyres start skidding on certain streets. The door handle are the house door's handle and they must transfer them back and forth every time they take the car, and the refrigerant liquid is leaking, but there's a funnel on the dashboard the driver has put an upside down bottle to automate filling canceling the leak.
Offering them a car that just works is in no way doubting their competence, but merely a catalyst for the change in state they want to happen. Consider it reducing the activation energy.
I gather from your other comment you have a couple of years of experience in machine learning. I suppose with real deadlines and money on the line, with colleagues working on the same project? Can you tell us more about your workflow? How do you deliver value without dealing with jammed windows and leaky reservoirs? Or how do you deal with that?
What's lacking? What's getting in your way? Why does the value take so long to reach end-users?
I'm sure a mechanic would figure out their broken car; it just is not ideal to do so everyday on their way to their new job/course.
Plus, in this case, it is not only about mechanics (devops). The window jams and doesn't close when it's raining. The fuel the car can fire changes every week. The seats were salvaged from a 1920's car. Seat belts are too tight and thin. The pedals are slippery. The sequence to turn on the car is in their colleague's head, and he's often absent. The tyres start skidding on certain streets. The door handle are the house door's handle and they must transfer them back and forth every time they take the car, and the refrigerant liquid is leaking, but there's a funnel on the dashboard the driver has put an upside down bottle to automate filling canceling the leak.
Offering them a car that just works is in no way doubting their competence, but merely a catalyst for the change in state they want to happen. Consider it reducing the activation energy.
I gather from your other comment you have a couple of years of experience in machine learning. I suppose with real deadlines and money on the line, with colleagues working on the same project? Can you tell us more about your workflow? How do you deliver value without dealing with jammed windows and leaky reservoirs? Or how do you deal with that?
What's lacking? What's getting in your way? Why does the value take so long to reach end-users?