Day 27: Tracking Large Artifacts – S3 and GCS Backends for MLflow

Day 27 focuses on how MLflow tracks and manages large machine learning artifacts using cloud-native object storage backends such as Amazon S3 and Google Cloud Storage. You’ll learn why local disk storage fails in production, how MLflow separates experiment metadata from artifact storage, and how object storage enables scalable, durable, and versioned model artifacts. The lesson includes a hands-on setup using MinIO to simulate real-world S3/GCS deployments and demonstrates how artifact storage supports reliable model deployment, rollback, and governance in production MLOps systems.

What This Lesson Covers
Why local disk–based artifact storage fails in real production systems
The difference between MLflow’s backend store (metadata) and artifact store (large files)
How object storage provides durability, scalability, immutability, and global access
How MLflow uploads, tracks, and retrieves artifacts using S3/GCS-style backends
How artifact URIs enable distributed training, deployment, and rollback
Why scalable artifact storage is essential for high-throughput systems (100M+ RPS inference environments)
Hashtags

#MLOps
#MLflow
#ArtifactStorage
#MachineLearning
#S3
#GCS
#ObjectStorage
#ProductionML
#MLInfrastructure
#ModelDeployment
#DataEngineering
#AIOps
#MLOpsEngineering

Видео Day 27: Tracking Large Artifacts – S3 and GCS Backends for MLflow канала Hands On Course Demo