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ML Systems Architecture

ML systems architecture encompasses the design of end-to-end pipelines that transform raw data into model predictions served at scale. Unlike traditional software, ML systems must manage both code artifacts (models)...

Shreedhar Kodate · 07 February 2025 · 1 min read
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ML systems architecture encompasses the design of end-to-end pipelines that transform raw data into model predictions served at scale. Unlike traditional software, ML systems must manage both code artifacts (models) and data artifacts (features, training datasets) with strict versioning and reproducibility requirements.

Overview

mindmap root((ML Systems\nArchitecture)) ML Pipelines Data ingestion Feature computation Training orchestration Evaluation Deployment Feature Stores Online store - low latency Offline store - historical Feature computation Feature sharing Feast Tecton Hopsworks Model Serving Online serving - REST gRPC Batch inference Streaming inference Shadow mode Multi-armed bandit Training Infrastructure Compute scheduling GPU cluster management Distributed training Hyperparameter optimization Experiment isolation

Topics in This Section

File Topic Key Concepts
01_ml_pipelines.md ML Pipelines Pipeline patterns, orchestration, DAGs
02_feature_stores.md Feature Stores Online/offline stores, feature reuse
03_model_serving.md Model Serving Online, batch, streaming serving
04_training_infrastructure.md Training Infrastructure GPU management, job scheduling