> One of my random question is that what does Google gain by spending resources on developing course like this?
Mindshare or more generally PR. Also to "collect" the talent on their platforms (Tensorflow, Google Cloud, ...). Also these guides were repurposed from existing (internal) guides and are a few years old by now, so the cost is low.
You further describe the role of a data engineer or ML engineer. If you'd approach data science with a focus on engineering and tool use, you could be one of the few dangerous data scientists that is able to go end-to-end (should be safe for at least 5 years when such pipelines are evolved without much human intervention).
> But when I see data science, no excitement. All I imagine is image manipulation and fancy charts.
This is because, while there is legit substance to the hype, the hype is real and it is focused on deep learning ImageNet (and later GAN's, Atari games, Go). Being able to show deepdreamed images and cat neurons is like catnip to journalists. Computer vision is but a very small part of ML and lots of data-driven companies have no need for such skills. Charts are made by analysts.
Everything (including block chain) will move closer to ML paradigm of learning software. Data infra engineers will see their infra increasingly used for ML. It remains all software (very advanced, but accessible to anyone) and hardware (still a asymmetry here between industry lab and practitioner). Don't get left out: Do machine learning like the great engineer you are, not like the great machine learning
expert you aren’t.
Mindshare or more generally PR. Also to "collect" the talent on their platforms (Tensorflow, Google Cloud, ...). Also these guides were repurposed from existing (internal) guides and are a few years old by now, so the cost is low.
You further describe the role of a data engineer or ML engineer. If you'd approach data science with a focus on engineering and tool use, you could be one of the few dangerous data scientists that is able to go end-to-end (should be safe for at least 5 years when such pipelines are evolved without much human intervention).
> But when I see data science, no excitement. All I imagine is image manipulation and fancy charts.
This is because, while there is legit substance to the hype, the hype is real and it is focused on deep learning ImageNet (and later GAN's, Atari games, Go). Being able to show deepdreamed images and cat neurons is like catnip to journalists. Computer vision is but a very small part of ML and lots of data-driven companies have no need for such skills. Charts are made by analysts.
Everything (including block chain) will move closer to ML paradigm of learning software. Data infra engineers will see their infra increasingly used for ML. It remains all software (very advanced, but accessible to anyone) and hardware (still a asymmetry here between industry lab and practitioner). Don't get left out: Do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.