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Machine learning: go full stack or go home | Sifted

ML is a relatively nascent industry with rudimentary tooling that is subject to rapid iteration. When this is the case, it’s often more efficient from a value creation standpoint to go the extra mile to become full-stack and control the value chain. To quote Henry Ford: “If you want it done right, do it yourself”. In contrast, when a market is mature, buyers are educated enough to outsource non-core functionality to third parties. In exchange for tolerable subscription fees, the buyer gains operational agility and improved overall product performance. This led to the success of API-first platforms like Twilio (communication), Stripe (payments) or Algolia (search). For ML this is likely to be many years away. Let’s not forget that It took the automobile and computer industry decades to disaggregate their supply chain.