ML solutions architecture

When I initially worked with companies as an ML solutions architect, the landscape was quite different from what it is now. The focus was mainly on data science and modeling, and the problems at hand were small in scope. Back then, most of the problems could be solved using simple ML techniques. The datasets were also small, and the infrastructure required was also not demanding. The scope of the ML initiative at these companies was limited to a few data scientists or teams. As an ML architect at that time, I primarily needed to have solid data science skills and general cloud architecture knowledge to get the job done.In the more recent years, the landscape of ML initiatives has become more intricate and multifaceted, necessitating involvement from a broader range of functions and personas at companies. My engagement has expanded to include discussions with business executives about ML strategies and organizational design to faciliate the broad adoption of AI...