Failure Modes, Trade-offs, Design Alternatives, and Anti-Patterns

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This section does not modify or replace any existing content. Instead, it extends each document in the project with production-level analysis. These extensions are intended to serve as a template for depth and to demonstrate how each file should be expanded as the documentation evolves.

1. Foundation & Architecture — Deep Analysis System Architecture Failure Modes

Tight coupling between training and serving can cause inference outages during retraining.

Single shared data store for online and offline workloads can introduce latency spikes and contention.

Lack of clear service boundaries leads to cascading failures across the system.

Trade-offs

Microservices vs Monolith: Microservices improve scalability and fault isolation but increase operational complexity.

Event-driven ingestion vs batch ingestion: Streaming improves freshness but increases system complexity and cost.

Design Alternatives

Monolithic ML service for early-stage systems

Fully decoupled, event-driven pipelines for mature platforms

Anti-patterns

Training models directly on production databases

Serving unversioned models

Embedding feature logic inside inference services

Infrastructure Design

Failure Modes

GPU starvation due to lack of workload isolation

Storage bottlenecks caused by insufficient I/O throughput

Credential leaks due to poor secret management

Trade-offs

Managed services vs self-hosted infrastructure: Managed services reduce operational burden but reduce control.

Autoscaling aggressiveness: Faster scaling improves latency but increases cost volatility.

Design Alternatives

Dedicated clusters for training and inference

Hybrid on-prem + cloud architectures

Anti-patterns

Running training and inference on the same node pool

Hardcoding secrets into deployment manifests

ML Pipeline Overview Failure Modes

Silent data schema changes causin