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I find this take interesting in the present. I work at $BIGCO that has done lots of ML in house for many years. I have noticed more and more ML projects being killed because their work is superseded by external model vendors. Why spend millions on a team to build a new model, when you can pay thousands to rent someone elses?

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It's a good question, and there are sometimes good answers. Does someone else's model understand how to standardize and interpret your company's data? Will they notice and tell you if something isn't looking right? What if the model needs to change to account for opportunities or risks from evolving data sources or business environment? Building in-house can help ensure that an organization has the competencies to do these things without depending on someone else.

Good point, it definitely isn't hands off from what I have seen. Just fewer people needed to manage those integrations.

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