The text discusses three prevalent challenges in machine learning and strategies to address them, based on experiences with companies like Uber and Etsy. The first challenge is building a model that is sufficiently effective to deliver business value, noting that although many models never reach production, successful deployment is feasible. The second challenge involves identifying a viable business use case where machine learning can add value, which requires collaboration between business leaders and data scientists. The third challenge is the inherent unpredictability of machine learning outcomes, which can be mitigated by adopting a portfolio approach where teams experiment with multiple projects to identify promising opportunities.