Startups need to think hard about creating a moat, or a deeper and more sustainable competitive advantage. Increasingly, leveraging collected data with Machine Learning (ML) is becoming more and more important to startups. Examples include algorithmic trading, shopping recommendation engines, content discovery, dynamic pricing and mobile media personalization. Every where you look, ML is becoming a key element to business differentiation. At Incoming the notion of ML being embedded in the phone (or app) drives the next generation of user personalization, which also respects user privacy.
Building ML teams and capabilities is complex. It’s yet another skill set to build into your team. The development loop for algorithm creation is working at a different pace than the engineering team. Figuring out how to build this capability into the startup may require deepening your thinking; it’s more than just hiring a data scientist. Figuring out how to bridge the gap between data science research and delivering a deployable service is a major new step. Algorithm selection for cold start, and robustness over say accuracy become major product engineering issues. One solution to consider may be partnering to build these competitive edges into your service or product.