Did Apple just commoditize machine learning? If it wasn't before...
The reason is simple. All of the work lies with validating that a model solves your particular problem well enough to be cost-effective for your customers or stakeholders to be satisfied. Machine learning is not a commodity the same way cloud infrastructure is, because the model development and validation aspects are inherently tailored to your extremely specific, one-off data generating processes and performance characteristic requirements.
Even if you outsource the model itself, this still requires developing some notion of acceptance testing for the model's performance in your use case, on the distribution of data that matter to you. And such acceptance testing still requires high literacy in statistics and model evaluation (e.g., you can't skip on hiring that expensive machine learning engineer even if you outsource because then who, internally, is going to be able to tell you if the outsource solution is a bunch of junk, and why, and what to do about it.)
Short of turning the big tech ML offerings into flat out consulting arrangements, where you trade-off having your own in-house and application-specific and data-specific machine learning staff in order to rent time from experts at the big co's, this model couldn't work.
Don't get me wrong, just as IBM somehow still finds ways to leach from enterprise clients after all these years of not actually helping them, I am sure these lines of business will generate enterprise consulting money.
I just think lots of people will be disappointed that it doesn't somehow mean you're getting Google ML engineer caliber attention or results spent on your company's bespoke needs, and that performance of generic and naive transfer learning from big cookie cutter models often ends up being way worse than you thought.
I feel bad for people who end up suffering the amplified version of vendor lock-in this could create.