A/B Testing Patterns for Machine Learning Models
Designing and running A/B tests for ML model changes. Traffic splitting, metric selection, statistical rigor, and common pitfalls.
AI Patterns
Design patterns and architectural patterns for AI-powered systems.
Reusable patterns for building reliable, scalable AI applications.
AI Audit Trail
Immutable logging of AI system decisions, inputs, outputs, and metadata for regulatory compliance, debugging, and accountability.
AI Gateway Pattern
Centralized gateway for routing, caching, rate limiting, and observability across multiple AI model providers. A single control plane for …
AI Supply Chain Security
Verifying model weights, scanning dependencies, and securing the end-to-end supply chain for AI artifacts from training to deployment.
AI System Decommissioning Pattern
A structured pattern for retiring AI models and systems, covering stakeholder notification, traffic migration, model archival, data cleanup, …
AI-Adapted Test Pyramid
The testing pyramid adapted for AI systems: unit tests for deterministic logic, integration tests with mocked models, evaluation tests with …
Audio Transcription Pipeline Patterns
End-to-end patterns for audio transcription at scale. Pre-processing, model selection, speaker diarization, and post-processing for …
Automated Compliance Monitoring for AI
Architecture pattern for continuous, automated monitoring of AI system compliance against GDPR, EU AI Act, NIS2, and organizational …
Batch Inference Patterns for AI Workloads
Processing large volumes of AI inference requests efficiently. Queue design, throughput optimization, error handling, and cost management …
Chain-of-Thought Prompting - Step-by-Step Reasoning for LLMs
Chain-of-thought prompting techniques that improve LLM performance on reasoning tasks by encouraging explicit intermediate steps.
Compliance as Code for AI Systems
Encoding regulatory requirements as automated checks: policy-as-code with OPA, automated audit trails, model governance, data privacy …
Continuous Training Pattern
Automated model retraining with promotion gates: scheduling strategies, data validation, evaluation pipelines, and safe production rollout.
Data Contract Pattern for AI Systems
Implementing schema contracts between data producers and AI consumers: contract specification, validation enforcement, versioning, and …
Data Flywheel Pattern
Production data continuously improves model performance, creating a compounding competitive advantage where better models attract more users …
Data Product Pattern
Treating data as a product with clear ownership, SLAs, documentation, and discoverability: organizational and technical patterns for …
Data Versioning
Git-like versioning for datasets: tracking changes, enabling reproducibility, supporting rollback, and managing dataset evolution across ML …
Differential Privacy for ML
Applying mathematical privacy guarantees during model training to prevent memorization of individual data points while preserving model …
Direct Model Interface - The Simplest AI Integration Pattern
The foundational pattern: user input goes to a model API, model response comes back. When this is enough and when you need something more.
Document Classification Patterns
Patterns for classifying documents by type, topic, sensitivity, and priority using AI. Multi-label classification, confidence handling, and …
Edge MLOps
Device-aware CI/CD for edge ML models: model optimization, over-the-air deployment, device fleet management, and monitoring at the edge.
Embedding Pipeline Patterns
End-to-end patterns for generating, storing, and querying embeddings at scale. Chunking strategies, vector database selection, and index …
Entity Extraction Patterns
Extracting structured entities from unstructured text using AI. Named entity recognition, relationship extraction, and schema-driven …
Evaluator-Optimizer Pattern
Automated evaluation loops where one model generates output and another evaluates it, driving iterative improvement until quality thresholds …
Explainability Pattern - Transparent AI Decision-Making
Middleware and architectural patterns for making AI decisions explainable, auditable, and trustworthy for users, regulators, and internal …
Explainability Service
On-demand model explanations for auditors, regulators, and end users: SHAP, LIME, attention visualization, and counterfactual explanations …
Fallback Chain Pattern
Cascading model fallback strategy where failures or low-confidence responses trigger automatic failover to alternative models, ensuring …
Fan-Out/Fan-In Pattern for AI Workloads
Parallel processing pattern for AI tasks: split work across multiple model calls, process concurrently, and aggregate results for faster …
Feedback Loop Pattern for AI Systems
Systematic collection and incorporation of user feedback to continuously improve AI model performance, prompt quality, and retrieval …
GDPR-Compliant ML Pipeline
Architecture pattern for building machine learning training and inference pipelines that satisfy GDPR requirements for data minimization, …
GPU Pooling
Shared GPU infrastructure with intelligent scheduling: maximizing GPU utilization across teams, managing heterogeneous hardware, and …
Graceful Degradation Patterns for AI Systems
Maintaining service quality when AI components fail or degrade. Fallback strategies, feature flags, cached responses, and partial …
Guardrails Pattern - Input and Output Safety for AI Systems
Implementing input validation, output filtering, and safety layers that prevent AI systems from generating harmful, off-topic, or …
Human-in-the-Loop Patterns for AI Systems
Design patterns for incorporating human review, approval, and correction into AI workflows. When to use HITL, how to implement review …
Image Classification Patterns for AI Applications
Patterns for building image classification systems. Multi-modal approaches, confidence handling, and production deployment strategies.
Lakehouse AI Pattern
Unified data architecture that combines data lake flexibility with data warehouse reliability for both analytics and AI workloads.
LLMOps Pipeline
Production pipeline design for LLM-specific operations: prompt management, evaluation, deployment, monitoring, and cost tracking across the …
Memory Patterns for Conversational AI - Short-Term and Long-Term
Architectural patterns for giving AI systems memory across conversations, from sliding context windows to persistent vector stores and user …
ML Feature Platform
Centralized feature computation, storage, and serving for ML systems: eliminating training-serving skew, enabling feature reuse, and …
Model Distillation Patterns for Production AI
Using large model outputs to train smaller, cheaper, faster models for specific tasks. When to distill, training approaches, and quality …
Model Ensemble Patterns for AI Applications
Combining multiple models for improved accuracy, reliability, and coverage. Voting, cascading, and specialization ensemble strategies.
Model Lineage Tracking
End-to-end tracking of data, code, hyperparameters, and artifacts across the ML lifecycle for reproducibility, debugging, and compliance.
Model Tier Routing - Matching Request Complexity to Model Cost
Route AI requests to different model tiers based on complexity, cost sensitivity, and quality requirements. Reduce spend without sacrificing …
Multi-Model Routing Patterns
Strategies for routing requests to different AI models based on task complexity, cost constraints, and latency requirements. Router design, …
Multi-Provider LLM Failover
Automatic failover between LLM providers for high availability: health checking, routing strategies, response normalization, and cost-aware …
Multi-Region Data Sovereignty Pattern
Architecture pattern for deploying AI systems across multiple regions while respecting data sovereignty requirements, covering data …
Multi-Tenant AI Architecture Patterns
Serving multiple customers from shared AI infrastructure while maintaining data isolation, fair resource allocation, and per-tenant …
Orchestrator-Worker Pattern
An orchestrator LLM decomposes complex tasks and delegates subtasks to specialized worker models or agents, coordinating results into a …
PII Redaction Pipeline
Automated detection and removal of personally identifiable information from LLM inputs and outputs: detection strategies, redaction methods, …
Plan-and-Execute Pattern - Separating Planning from Execution in AI Agents
A two-phase agent pattern where a capable planner model creates a step-by-step plan, then delegates each step to cheaper, faster executor …
Policy as Code for ML
Executable governance rules in ML CI/CD pipelines: automated compliance checks, deployment gates, and enforceable organizational policies …
Privacy-Preserving AI Pattern
Architecture patterns for building AI systems that protect data privacy, covering federated learning, differential privacy, secure …
Progressive Delivery for AI Deployments
Combining feature flags, canary releases, and automated rollback for AI model deployments: AI-specific metrics, shadow mode testing, and …
Prompt Injection Defense
Layered defense strategies against prompt injection attacks in production LLM applications: input validation, output filtering, privilege …
Prompt Template Management Patterns
Version control, testing, and deployment patterns for managing prompt templates at scale. Treating prompts as code.
Rate Limiting Patterns for AI Applications
Implementing effective rate limiting for AI-powered applications. Token-based limits, adaptive throttling, queue management, and fair …
ReAct Pattern - Reasoning and Acting in AI Agents
The ReAct pattern interleaves chain-of-thought reasoning with tool actions, enabling AI agents to think before they act and adjust based on …
Real-Time Feature Computation Pattern
The architectural pattern for computing ML features from event streams: windowed aggregations, stream-table joins, dual-write to online and …
Real-Time Feature Serving
Sub-millisecond feature serving for online inference: architecture, caching strategies, precomputation patterns, and consistency guarantees.
Real-Time vs Batch AI Processing - Choosing the Right Pattern
Decision framework for choosing between real-time and batch AI processing. Latency requirements, cost tradeoffs, hybrid architectures, and …
Reflection Pattern - Self-Critique and Iterative Refinement for LLMs
Using self-reflection loops where an LLM evaluates and improves its own output, catching errors and improving quality without human …
Response Streaming Patterns for AI Applications
Implementing streaming responses from LLMs for improved perceived latency. Server-sent events, chunked processing, and progressive …
Retrieval Routing Pattern
Smart routing between multiple knowledge sources based on query intent, selecting the optimal retrieval strategy for each request across …
Sandbox Testing Pattern for AI Agents
Sandboxed execution environments for testing AI agents with real tool access without production side effects: isolation strategies, resource …
Self-Healing Architecture - AI-Powered Automated Recovery
Using AI to detect, diagnose, and automatically remediate infrastructure and application failures without human intervention.
Self-Healing Model Pattern
Automated drift detection, performance monitoring, and retraining triggers that keep ML models healthy in production without manual …
Semantic Assertion Pattern
Asserting AI output correctness via semantic similarity rather than exact string match: embedding-based comparison, LLM-as-judge, and …
Semantic Caching for AI Applications
Caching AI model responses based on semantic similarity rather than exact match. Implementation patterns, cache invalidation, and …
Sentiment Analysis Pipeline Patterns
Building production sentiment analysis pipelines. Multi-dimensional sentiment, aspect-based analysis, and real-time monitoring at scale.
Shadow Deployment Pattern for AI Models
Running new AI models in parallel with production models to compare outputs without affecting users. Implementation, comparison strategies, …
Statistical Assertion Pattern
A testing pattern for non-deterministic AI outputs: run N times, assert success rate exceeds threshold, use confidence intervals to account …
Structured Output - Enforcing JSON and Schema Compliance from LLMs
Techniques for getting reliable, machine-parseable structured output from LLMs: JSON mode, schema enforcement, constrained decoding, and …
Summarization Chain Patterns
Multi-step summarization strategies for long documents. Map-reduce, hierarchical, and iterative refinement approaches for reliable AI …
Token Optimization Patterns for LLM Applications
Strategies for reducing token usage without sacrificing output quality. Prompt compression, context pruning, output formatting, and cost …
Tool Use Pattern - Function Calling for AI Agents
Enabling LLMs to invoke external tools and APIs through function calling, extending model capabilities beyond text generation.
Translation Pipeline Patterns
Building production translation pipelines with AI. Terminology management, quality assurance, and multi-language orchestration patterns.
VCR Pattern for AI API Testing
Record-and-replay pattern for AI API testing: capture real model responses once, replay them in CI for deterministic, fast, and free tests.
Vector Index Management
Lifecycle management for vector embeddings: index building, versioning, refresh strategies, quality monitoring, and operational practices …
Vector Search Optimization Patterns
Improving vector search quality and performance. Index tuning, hybrid search, re-ranking, and query optimization for production RAG systems.
Video Analysis Pipeline Patterns
Architecture patterns for AI-powered video analysis. Frame extraction, multi-modal analysis, temporal alignment, and cost management …
Zero Trust for AI Model Serving
Applying zero trust architecture to AI systems: securing inference endpoints, model artifact access, training data, and service-to-service …
Caching Patterns for AI Applications
Semantic caching, Anthropic prompt caching, response caching, and embedding caching for AI applications. Cost savings analysis and …
Retry and Backoff Patterns for AI Services
Exponential backoff with jitter, retry budgets, and idempotency patterns for production AI systems. Why AI services require different retry …
Tiered Analysis Pattern - Progressive Depth for AI Processing
Apply cheap analysis first, score results, then apply expensive analysis only to candidates that pass a threshold. Reduces AI API costs by …
Blue-Green Deployment for AI Services
Zero-downtime model updates using blue-green deployment: how it works, AWS implementation with Lambda aliases and SageMaker variants, and …
Canary Deployment for AI Models
Gradual traffic shifting to new model versions: how to implement canary deployments with Lambda weighted aliases and SageMaker production …
Circuit Breaker Pattern for AI Services
Handling model failures gracefully in production AI systems: fallback strategies, degraded mode operation, retry with backoff, and …
Event Sourcing and CQRS for AI Pipelines
Using event-driven architecture patterns for AI data pipelines: immutable event logs, replay capability, audit trails, and CQRS for …
Feature Flags for AI Model Deployment
Using feature flags to safely roll out AI model changes: A/B testing models, canary deployments, gradual traffic shifting, and instant …
Microservices Architecture for AI Systems
How to decompose AI systems into independent services with clear boundaries, API contracts, and independent deployability - treating AI …
Model Versioning and Artifact Management
Why model versioning matters and how to implement it: S3 for artifacts, Git for configuration, SageMaker Model Registry, Bedrock model …
Observability for AI Systems - Logs, Metrics, Traces
Applying the three pillars of observability to AI workloads: CloudWatch for metrics and alarms, Langfuse for LLM tracing, OpenTelemetry for …
Strangler Fig Pattern for AI Migration
How to gradually replace manual processes and legacy rule-based systems with AI using the strangler fig pattern: routing traffic …
Agentic Workflow Patterns - From Simple Chains to Complex Orchestration
Chain, router, parallel, hierarchical, and loop patterns for AI agents. When to use each, error handling, and fallback strategies.
AI Cost Optimization Patterns
Model selection by task, caching strategies, batch vs real-time processing, and tiered inference with Haiku, Sonnet, and Opus.
AI Governance Patterns for Enterprise
Model cards, decision logging, bias detection, approval workflows, audit trails, compliance documentation, and EU AI Act considerations.
Context Window Management Patterns
Summarization, sliding window, retrieval-augmented, and hierarchical context patterns for handling conversations and documents that exceed …
Data Pipeline Patterns for AI/ML Workloads
Practical patterns for building reliable data pipelines that feed AI and ML systems - ingestion, transformation, feature engineering, and …
Evidence Bundling Pattern - Collecting and Organizing Proof for AI Decisions
How to design AI systems that collect, organize, and present evidence for their recommendations. Critical for regulated industries and any …
Prompt Engineering Patterns for Enterprise Applications
Proven prompt patterns for enterprise AI applications: structured output, chain-of-thought, few-shot examples, guardrails, and system prompt …
RAG Implementation Patterns - Retrieval Augmented Generation in Practice
Practical patterns for building production RAG systems: chunking strategies, retrieval optimization, re-ranking, and the most common failure …
Scoring and Prioritization Patterns for AI Systems
Different scoring approaches for AI-driven prioritization - WSJF, opportunity/effort matrix, risk-adjusted scoring - when to use each, and …
The Intake-to-Action Pattern - Structured Data from Unstructured Input
A reusable pattern for converting unstructured inputs - forms, emails, documents - into structured data with risk flags and suggested next …