MLOps Systems at Scale with Krishna Gade

 Although we like to think about ML workflows as straight-line narratives from experiment to training to production, and then finally monitoring; the reality for large companies is that all the steps are happening at one time in concert with other models, with shifting data, and, sometimes, misaligned key feature inputs. Moreover, regulated firms are required