ML engineering applies a system around this staggering level of complexity. It uses a set of standards, tools, processes, and methodology that aims to minimize the chances of abandoned, misguided, or irrelevant work being done in an effort to solve a business problem or need. It, in essence, is the road map to creating ML-based sys- tems that can be not only deployed to production, but also maintained and updated for years in the future, allowing businesses to reap the rewards in efficiency, profitabil- ity, and accuracy that ML in general has proven to provide (when done correctly).