AI/ML System Design

Architectural patterns for machine learning systems and AI applications. Explore MLOps, model serving, feature stores, and other critical components for production ML systems.

AI/ML Architecture Patterns

Explore different architectural patterns for building scalable, reliable, and efficient machine learning systems. Each pattern includes detailed explanations, trade-offs analysis, and implementation guidance.

Found 3 architectures

MLOps Pipeline

End-to-end machine learning lifecycle management from data ingestion to model deployment and monitoring.

High Complexity

MLflowKubeflowAirflowDVCWeights & Biases+1 more

Latency:Low (minutes to hours for training)

Throughput:High (parallel training)

Scalability:Excellent

Cost:High

Key Trade-offs:

Complexity:High operational complexity and tooling

Reproducibility:Excellent experiment tracking and reproducibility

Cost:Infrastructure and tooling costs

Use Cases:

Production ML systemsLarge-scale model trainingTeam collaboration+2 more

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MLOps

Model Serving Architecture

Scalable infrastructure for serving machine learning models in production with high availability and low latency.

High Complexity

TensorFlow ServingTorchServeSeldon CoreKubernetesRedis+1 more

Latency:Very Low (milliseconds)

Throughput:Very High (thousands of requests/sec)

Scalability:Excellent

Cost:Medium to High

Key Trade-offs:

Latency:Optimized for low-latency inference

Resource Usage:Models loaded in memory for fast access

Scalability:Horizontal scaling with load balancing

Use Cases:

Real-time predictionsRecommendation systemsComputer vision APIs+2 more

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Model Serving

Feature Store Architecture

Centralized system for storing, managing, and serving machine learning features for training and inference.

High Complexity

FeastTectonHopsworksRedisPostgreSQL+1 more

Latency:Low (milliseconds for online, hours for offline)

Throughput:Very High (millions of features/sec)

Scalability:Excellent

Cost:High

Key Trade-offs:

Data Consistency:Ensures feature consistency across training and inference

Complexity:Additional infrastructure and operational overhead

Performance:Optimized feature retrieval for ML workloads

Use Cases:

Large-scale ML systemsFeature reuse across modelsReal-time feature serving+2 more

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Feature Store

More AI/ML patterns coming soon!

• A/B Testing• Data Versioning• Federated Learning• AutoML