Our research aims to identify the existing product-engineering methods and practices adopted in the industry for building applications and platforms relying on machine learning. We conducted a Grey Literature Review (GLR) to investigate and discuss the methods and practices applied to the ML product lifecycle from the industry perspective. We mapped 58 practices and methods in 6 categories related to the processes of designing, developing, testing, and deploying ML products. It is crucial to guide product managers, data scientists, and software engineers to better understanding the challenges of ML product lifecycle.