IMO it's a great educational tool to understand how to build AI apps at least at a high level. It provides a good mental model. Can someone name another single resource that achieves the same?
As aspiring data scientists or data scientists new to the field (and working in a business-centric rather than a research-centric role), it is easy to be blinded by the technical aspects of data science such as exploratory data analysis and machine learning, failing to see the forest for the trees.
My goal with this post and accompanying GitHub project is to provide a more accurate example of an enterprise-grade data science project that includes what is often missed in these sorts of examples: the rigorous positioning of a business problem and scoping of a data science solution.
The majority of the time spent to complete this project was allocated to the understanding and definition of the business problem rather than optimizing a machine learning model. As with any other complex problem, spending some time upfront to work through the various aspects of a business problem in a systematic way will reduce the risk of coming up with the wrong solution, which clearly wastes time (read: opportunity loss) and money.
Right, that'd be a big leap since we also plan to make it viable for non-tech users. If we put those kinds of feature in plugin, we can keep the app simple & clean.
And even if every business did need an image classifier, they would still need a technical person that understands how this AutoML model they train can be integrated into business processes or software applications...
I think "making AI accessible to every business" is a bit of stretch. While there's no doubt that the AutoML suite will bring tremendous benefits to businesses with recommendation and speech and image recognition needs, it falls short of providing more useful insights such as those gleaned by association rules, clustering (i.e. segmentation), and general probabilistic models.
I think that if AI is to be accessible to every business then it will deliver insights rather than the machinery to produce the insights. This is especially true in the context of small businesses.
I'm not personally looking for the insights myself, just an observation from working with SMBs trying to leverage data science more generally to improve their businesses.
And I'm not discrediting the work or the fact that this is progress... I just don't think that the title of the post is entirely accurate. Mostly with respect to the "every" part.
> it falls short of providing more useful insights such as those gleaned by association rules, clustering (i.e. segmentation), and general probabilistic models.
I would also hesitate to build a business relying on Google for those things since I'd likely be competing with Google's actual moneymaker.
Very very occasionally, like when the wife & kids are out of town and I have the place to myself. I've smoked maybe 5 joints since I quit. Unfortunately I still indulge in other substances more than I'd like to.
