ML Pipeline Stack (PyTorch/TensorFlow, MLflow, BentoML, K8s) overview

A production machine learning pipeline stack: PyTorch or TensorFlow as the training framework, MLflow for experiment tracking, model registry, and reproducibility, BentoML for packaging models into deployable services, Kubernetes for orchestrating training jobs and serving endpoints, and Prometheus for monitoring model performance and infrastructure health. The pipeline spans from data preparation through training, evaluation, packaging, deployment, and monitoring. MLflow tracks hyperparameters, metrics, and artifacts across experiments. BentoML wraps trained models into containerized REST/gRPC services with adaptive batching. Kubernetes provides autoscaling for both training (via jobs or operators like KubeFlow) and inference (via Deployments with HPA). Prometheus scrapes model latency, throughput, and data-drift metrics. This stack is common in organizations that have graduated beyond notebook-driven ML and need repeatable, observable, production-grade model lifecycle management.