The Engineering Reality of Production MLOps
For a long time, MLOps has been described as “DevOps for machine learning.” That description works—right up until your systems reach scale. At that point, it breaks. In real production environments, model behavior, infrastructure cost, and system reliability are tightly coupled. You can’t optimize one without understanding the others. This is where many ML platforms quietly fail—not because of bad models, but because of incomplete engineering thinking. This post explains why MLOps is its own senior engineering discipline , and why effective MLOps engineers must understand both systems and machine learning dynamics . Training Is a Systems Workload Every training job follows the same fundamental loop: Forward pass → loss computation → backward pass → weight update That loop directly dictates how hardware is stressed. Increasing batch size increases GPU memory linearly Poor data loading leaves expensive GPUs idle Distributed training bottlenecks often come from network synchroni...