MLOps for Classical Machine Learning is a practical engineering guide to getting tabular, time-series, and structured-data models into production and keeping them there. It starts from a single observation: the overwhelming majority of production machine learning systems in the world are not LLMs, and the discipline they need is not the discipline the industry has been talking about for the last three years.
The book walks through the full lifecycle — the case for classical MLOps as its own discipline, the seven stages of the machine learning lifecycle, feature engineering at scale, feature stores and consistent training-serving pipelines, experiment tracking and reproducibility, training pipelines and orchestration, hyperparameter optimization, model registries and versioning, deployment patterns from real-time to batch to edge, monitoring and drift detection and retraining, evaluation and validation and governance, MLOps on Kubernetes and in the cloud, the organisational structures that make the practice sustainable, and the trends reshaping the field.
It covers the failure modes that quietly wreck production ML systems: a feature computed differently in training and serving that silently degrades model quality, a training pipeline that produces non-reproducible models because the random seed is not fixed, a hyperparameter search that converges on a configuration that overfits the validation set, a drift detector that fires constantly because the threshold was set without measuring the baseline, a feature store that serves stale values in online mode because the materialisation job fell behind, a governance process that documents the model at launch and never updates it. Each is presented with the failure, the countermeasure, and the operational tradeoff.
