The machine learning (ML) industry has come a long way since its inception fifty years ago, with ML now being an integral part of society and helping various aspects of life such as driving cars, job searching, loan approvals, and medical treatments. The future trajectory of the industry is uncertain but there are emerging tools and capabilities that are becoming standards for nearly every ML initiative. Beyond these tools, the roles that shape data teams are rapidly evolving, particularly in the area of ML ops, which involves integrating development and operational aspects of ML infrastructure. This has led to the emergence of a new class of expertise - the ML engineer, who bridges the gap between data scientists and operations teams to ensure models perform well once they leave the lab. Companies need to invest in ML engineers to overcome challenges such as performance degradation issues with models that don't perform after code is shipped, requiring both tools for model observation and teams understanding how to make them perform.