If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there.
"Everyone wants to do modeling, but no one wants to do the engineering," highlights a stark reality in the machine learning (ML) world: the allure of building sophisticated models often overshadows the critical task of engineering them into robust, scalable, and efficient systems.
The reality is that ML and systems are inextricably linked. Models, no matter how innovative, are computationally demanding and require substantial resources—with the rise of generative AI and increasingly complex models, understanding how ML infrastructure scales becomes even more critical. Ignoring the system's limitations during model development is a recipe for disaster.
Unfortunately, educational resources on the systems side of machine learning are lacking. There are plenty of textbooks and materials on deep learning theory and concepts. However, we truly need more resources on the infrastructure and systems side of machine learning. Critical questions—such as how to optimize models for specific hardware, deploy them at scale, and ensure system efficiency and reliability—are still not adequately understood by ML practitioners. This lack of understanding is not due to disinterest but rather a gap in available knowledge.
Source: TensorFlow Blog — Read the full article on the original site ↗