Not to generate impression of bias toward Azure, refer to the post 04.27.2021

https://www.aapg.org/care...net/activity/aft/524

“GCP - geek-less AutoML Tables is a very nice user-friendly ML frontend without us writing a single line of code.“

For completeness, AWS SageMaker does similar things and delivers Jupyter Notebook with codes running the AutoML.

Perspective - it all boils down to our business need, a) one-off simple execution to get a result, b) building workflow process and c) replicating codes for transfer learning application.

Bottom line - AWS, Azure and GCP are all viable options. The swing decision factor may simply hinge on where our data resides (in the cloud or on premise). What we really want is connecting data to tackle challenges, identify hurdles and solutions, engage communities and harvest value, right?

If so, mark your calendar the upcoming Energy in Data event in November and watch Inbox for detail from co-Chair Susan Nash. Stay tuned.

Meanwhile, feel free to share your machine learning journey and keep learning.