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Quick Definition
MLOps applies DevOps principles to machine learning, automating the lifecycle of building, training, deploying, and monitoring models. It addresses reproducibility, pipelines, and model performance, and increasingly incorporates cost management for compute-heavy training and inference.
MLOps applies the discipline of DevOps to machine learning: versioning data and models, automating training pipelines, deploying models safely, and monitoring them in production for both technical health and prediction quality, since models silently decay as the world drifts away from their training data.
ML workloads have a distinctive cost profile. Training is bursty and GPU-hungry; inference is continuous and scales with traffic; data pipelines and storage grow relentlessly. Spend hides in idle GPU notebooks, over-provisioned inference endpoints, retraining jobs that run more often than the model improves, and experiment artifacts kept forever.
Example. A team audits its ML platform and finds inference endpoints provisioned for launch-day traffic running at 8 percent utilization, plus $6,000 monthly in forgotten experiment storage. Autoscaling endpoints and expiring artifacts recovers a third of the platform bill.
Treat cost as an MLOps metric beside accuracy and latency: cost per training run, per thousand predictions, per model. The AI Cost Optimization Guide and lessons from real AI cost incidents cover the practice.
ML adds data and model versioning, retraining pipelines, and quality monitoring. Code is no longer the only thing that changes.
Idle GPUs, oversized inference endpoints, frequent retraining with little gain, and ever-growing datasets and artifacts.
Cost per training run, cost per thousand inferences, GPU utilization, and storage growth rate per project.