MLOps

Definition

MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy, monitor, and maintain ML models in production reliably and efficiently. MLOps addresses the unique challenges of ML systems: data dependencies, model drift, training/serving skew, reproducibility, and the need for continuous retraining.

Why It Matters

Building a machine learning model in a Jupyter notebook is relatively straightforward. Deploying that model to production, monitoring its performance, detecting when it degrades, retraining it with new data, and managing multiple model versions across environments -- that is where most ML initiatives fail.

Studies consistently show that 85-90% of ML models never reach production. The gap is not model quality. It is operational infrastructure. MLOps closes this gap by applying DevOps principles to the ML lifecycle: version control for data and models, automated training and deployment pipelines, monitoring for model performance drift, and governance frameworks for model risk management.

For enterprises, MLOps is the difference between ML as a research project and ML as a production capability. Without it, AI initiatives produce interesting experiments that never deliver business value.

Key Practices

Data versioning -- Track and version the datasets used for training, enabling reproducibility
Experiment tracking -- Log hyperparameters, metrics, and artifacts for every training run
Model registry -- Central catalog of trained models with version history, metadata, and approval workflows
Automated training pipelines -- Triggered retraining based on data freshness, performance degradation, or schedule
Model serving -- Scalable inference infrastructure with A/B testing and canary deployments
Model monitoring -- Track prediction accuracy, data drift, concept drift, and latency in production
Governance -- Model risk management, bias detection, explainability, and audit trails

How BAG Uses It

Our AI platform implements MLOps practices across all model deployments: automated training pipelines, model versioning, performance monitoring, and cost tracking per model. We architect MLOps infrastructure for clients using tools ranging from managed platforms (SageMaker, Vertex AI) to open-source stacks (MLflow, Kubeflow), selected based on the client's scale, team, and requirements.

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