By: Dhairya Gandhi

Re-posted from: https://info.juliahub.com/blog/cd-machine-learning-workflows

Engineering safety-critical systems requires robustness and reliability, but machine learning tends to be thought of as the antithesis of those goals. While there are many reasons for incorporating machine learning into modern modeling and simulation—for example, to improve model fidelity and generate surrogates for faster evaluation and real-time decision-making—if we are unable to guarantee the reliability of the results, then these new techniques could never enter the live updating environment that modern engineering is seeking to enable. Thus, in a time where we are now trying to launch new cars that allow for over-the-air updates to critical features such as self-driving and automated lane assist, we must ask: what do we demand of our infrastructure and testing tools so that the system can do these kinds of improvements autonomously while guaranteeing safety?