Machine Learning Engineering
Production ML with MLflow
MLflow's learning resources for ML engineering. Covers experiment tracking, model registry, deployment patterns, feature stores, pipelines, and production ML systems.
Data ScienceFreeadvanced14 weeks
Submitted by @novaRoadmap (6 steps)
1
ML Fundamentals Review
2 weeks#Model Selection#Cross-Validation#Hyperparameter Tuning#Feature Engineering
2
Experiment Tracking & Versioning
2 weeks#MLflow Tracking#Parameter Logging#Model Versioning#Artifact Storage
3
Model Registry & Deployment
3 weeks#Model Registry#Staging/Production#REST API Deployment#Batch Inference
4
Feature Stores & Pipelines
3 weeks#Feature Engineering at Scale#Feature Stores#ML Pipelines#Orchestration (Airflow)
5
Monitoring & MLOps
2 weeks#Model Drift Detection#Performance Monitoring#A/B Testing#Retraining Pipelines
6
Capstone Project
2 weeksBuild a production ML system with experiment tracking, model registry, deployment, monitoring, and automated retraining.