Many AI projects fail due to the lack of MLOps expertise to help put ML in production. Companies realize that building successful AI products requires data engineers, scientists, ML engineers, and product managers to work in a team.
ML Engineers play an essential role in putting models in production and ensuring models can be continuously integrated and deployed (CI/CD) with high-quality data (data-centric AI).
If you currently work as a Data Scientist, Software Engineer, or DevOps professional, ML Engineer could be a great natural progression.
MLOps’ most important task is to make high quality data available through all stages of the ML project lifecycle.
-Andrew Ng on data-centric AI-