The text discusses the challenges and strategies of sharing and reusing features across different machine learning (ML) models in an organization. It highlights that companies with tens of thousands of ML models in production, like Uber, Twitter, or Google, have successfully shared and reused features to scale their operational ML capabilities. The key strategy for successful feature reuse is to share and reuse features across models and use cases, which allows teams to leapfrog over the most difficult parts of putting new models into production. However, this requires addressing common challenges such as lack of knowledge about existing features, unclear ownership, and poor pipeline visibility. The text proposes three approaches for sharing features: publishing feature data to a shared location, sharing feature pipelines, and implementing a feature store. Each approach has its pros and cons, and the choice of approach depends on the organization's specific needs and requirements.