AIRA Wiki: AI Reference Architectures

Core Content Sections

1. Cloud ML Pipeline Architectures

Enterprise-ready blueprints for building scalable machine learning workflows across major cloud platforms. These architectures incorporate MLOps best practices for CI/CD, monitoring, and governance:

• AWS ML Pipeline - End-to-end architecture leveraging AWS SageMaker, Step Functions, and native AI services
• Azure ML Pipeline - Microsoft Cloud implementation using Azure ML, Databricks, and Synapse integration
• GCP ML Pipeline - Google Cloud solution with Vertex AI, Kubeflow, and BigQuery ML components

2. AI Model Reference Architecture (8 Categories)

Our comprehensive framework for classifying AI/ML implementations across the development lifecycle, with maturity benchmarks for enterprise adoption:

1. Traditional Machine Learning
   Production patterns for regression, classification, and clustering algorithms in business applications
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2. Deep Learning Systems
   Reference implementations for neural networks in computer vision, speech, and complex pattern recognition
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3. Reinforcement Learning
   Architectures for autonomous systems, game AI, and adaptive control mechanisms
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4. Time Series Analysis
   Specialized frameworks for forecasting, anomaly detection, and temporal pattern recognition
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5. Generative AI
   Production architectures for LLMs, diffusion models, and synthetic data generation systems
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6. Agentic AI
   Emerging patterns for autonomous AI agents with reasoning and decision-making capabilities
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7. AI Infrastructure & Emerging Tech

Explore the foundational and advanced design patterns that power enterprise-grade computer vision systems. This reference architecture covers critical components such as data ingestion, preprocessing, model training, deployment strategies, edge-cloud orchestration, and privacy-aware governance. Whether you're building image classification pipelines, object detection models, or real-time video analytics solutions, this guide offers a modular and scalable blueprint for successful implementation. Explore →

8. Computer Vision

Discover how modern enterprises are designing, deploying, and scaling Computer Vision systems using modular, cloud-ready architectures. This reference blueprint covers everything from image ingestion and preprocessing to model training, inference pipelines, and ethical deployment considerations. Whether you're working with object detection, medical imaging, or real-time video analytics, this guide offers a structured and future-proof approach.

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9. Speech Audio Processing

Explore the foundational elements of voice-driven AI, including real-time transcription, advanced audio analytics, and acoustic signal processing. This section delves into architecture patterns, best practices, and scalable pipeline design for cutting-edge applications such as: ✅ Speech Recognition – Accurately transcribe spoken language in real-time. ✅ Speaker Identification – Differentiate and recognize individual voices with precision. ✅ Emotion Detection – Analyze vocal tone and patterns to assess sentiment and mood. ✅ Audio Event Classification – Detect and categorize meaningful sound events for enhanced insights.

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10. Multimodal AI

Unlock the full potential of Multimodal AI with a structured, production-ready reference architecture designed for enterprise applications. This comprehensive guide provides a layered approach to integrating text, image, audio, video, and sensor data—ensuring seamless fusion and orchestration. 🔹 Key System Layers Covered: ✅ Data Ingestion – Efficiently process multimodal inputs from diverse sources. ✅ Encoding & Representation – Optimize feature extraction across modalities. ✅ Fusion Strategies – Enhance cross-modal learning for richer insights. ✅ Orchestration & Scalability – Streamline workflows with advanced automation. Supported by detailed Mermaid diagrams and best-practice design principles, this guide equips you with practical insights for building multimodal agents, cross-modal search, and generative AI systems at scale.

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Key Features

• Standardized Architectures: Battle-tested patterns from enterprise implementations
• Maturity Benchmarks: Clear progression metrics for each AI category
• Production Focus: Emphasis on deployable, maintainable systems
• Vendor-Neutral Guidance: Adaptable across cloud providers and on-premise deployments

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