What formula should we really pay attention to build a successful Machine Learning Model ?
Lots of data science projects are doomed to failure before it begins. Vague problem statements and vague success criteria kill a project. What formula or golden rule we must follow ? Here is Google Cloud Platform's Field Sales Representative - Patrick Leung's experience and insights!
Quantify success and failure is the first step of making a successful ML (Machine Learning) Model. Frame the problem and adopt the scientific method are some important concepts to cultivate the ML mindsets for your business.
Field Sales Representative
Google Cloud Platform