Introduction: Model deployment represents the critical bridge between ML experimentation and business value, yet remains one of the most challenging aspects of production ML systems. This comprehensive guide explores deployment patterns from REST APIs and batch inference to edge deployment and A/B testing frameworks. After deploying hundreds of models across diverse environments, I’ve learned that… Continue reading
Category: MLOps
Feature Engineering at Scale: Building Production Feature Stores and Real-Time Serving Pipelines
Introduction: Feature engineering remains the most impactful activity in machine learning, often determining model success more than algorithm selection. This comprehensive guide explores production feature engineering patterns, from feature stores and versioning to automated feature generation and real-time feature serving. After building feature platforms across multiple organizations, I’ve learned that success depends on treating features… Continue reading
MLOps Excellence with MLflow: From Experiment Tracking to Production Model Deployment
Introduction: MLflow has emerged as the leading open-source platform for managing the complete machine learning lifecycle, from experimentation through deployment. This comprehensive guide explores production MLOps patterns using MLflow, covering experiment tracking, model registry, automated deployment pipelines, and monitoring strategies. After implementing MLflow across multiple enterprise ML platforms, I’ve found that success depends on establishing… Continue reading