Machine Learning Operations (MLOps)

While DevOps focuses on continuous integration and deployment for traditional software code, MLOps specifically handles the complexities of machine learning lifecycles. MLOps must account for versioning not just code, but also datasets and model weights, as well as monitoring for data drift in production. It bridges the gap between data science models and reliable production reality.

Popular tools for creating model serving endpoints include FastAPI for lightweight, custom REST APIs, and NVIDIA Triton Inference Server for heavier, GPU-accelerated deployments. These tools wrap the trained model so that downstream applications can send real-time data and receive predictions. The choice of tool often depends on the required latency and the specific deep learning framework used.

Model drift occurs when the statistical properties of the target variables or input data change over time in the real world, degrading the model's accuracy. MLOps pipelines address this by setting up automated monitoring systems that trigger continuous retraining when accuracy drops below a certain threshold. Without this monitoring, models silently fail as real-world data evolves beyond their original training scope.

Model versioning tracks exactly which dataset, hyperparameters, and code were used to generate a specific deployed model. This is crucial for governance and auditing, allowing engineers to instantly roll back to a previous version if a newly deployed model starts making erratic predictions. It ensures full reproducibility, which is strictly required in heavily regulated industries like finance or healthcare.