MLOps Pipelines: Automating the ML Lifecycle
MLOps brings DevOps practices to machine learning. This guide implements production ML pipelines with Kubeflow and MLflow.
MLflow Experiment Tracking
Track experiments with automatic logging:
Kubeflow Pipeline
Orchestrate end-to-end ML workflow:
Data Versioning with DVC
Version control for datasets:
Continuous Training
Automatically retrain on new data:
Warnings ⚠️
Pipeline Complexity: Multi-stage pipelines accumulate failure modes. The 2034 "Pipeline Cascade" occurred when 300-step ML pipelines became impossible to debug.
Hidden Dependencies: Data lineage tracking fails, causing silent data quality issues.
Automation Runaway: Continuous training without human oversight deployed progressively worse models for weeks before detection.
Related Chronicles: The MLOps Meltdown (2034) - Automated systems deploying broken models
Tools: Kubeflow, MLflow, DVC, Airflow, Prefect, Weights & Biases
Research: Continuous learning systems, online learning, model monitoring