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AI Project Architecture

This document outlines the architecture of our AI system, detailing the components, data flow, and infrastructure that powers our machine learning capabilities.

System Overview​

Our AI architecture is designed to support the full lifecycle of machine learning models from data collection to deployment and monitoring. The system follows modern MLOps practices to ensure reliable, scalable, and maintainable AI services.

AI System Architecture

Core Components​

1. Data Ingestion Layer​

The data ingestion layer handles the collection and initial processing of data from various sources:

  • Data Sources:

    • User interaction logs from our applications
    • Content metadata from our databases
    • Third-party data providers
    • Labelled datasets from our Labelling Platform

  • Ingestion Pipelines:

    • Batch processing for historical data
    • Stream processing for real-time data
    • Data validation and quality checks

  • Technologies:

    • Apache Kafka for streaming data
    • Airflow for batch processing workflows
    • Great Expectations for data validation

2. Data Processing Layer​

The data processing layer transforms raw data into features suitable for machine learning:

  • Feature Engineering:

    • Text preprocessing for NLP tasks
    • Image transformations for computer vision
    • Feature extraction and selection
    • Feature normalization and encoding

  • Feature Store:

    • Centralized repository of features
    • Versioning and lineage tracking
    • Serving features for both training and inference

  • Technologies:

    • Feast for feature storage and serving
    • Apache Spark for distributed processing
    • Python data processing libraries (Pandas, NumPy)

3. Model Training Infrastructure​

The model training infrastructure handles the development and training of machine learning models:

  • Experiment Tracking:

    • Hyperparameter management
    • Metrics tracking and comparison
    • Model versioning

  • Distributed Training:

    • GPU/TPU resource orchestration
    • Multi-node training capabilities
    • Checkpointing and recovery

  • Technologies:

    • MLflow for experiment tracking
    • Kubernetes for container orchestration
    • TensorFlow and PyTorch for model development
    • Weights & Biases for visualization

4. Model Registry​

The model registry manages trained models throughout their lifecycle:

  • Model Versioning:

    • Semantic versioning of models
    • Model metadata storage
    • Artifact management

  • Model Governance:

    • Approval workflows
    • Deployment policies
    • Access control

  • Technologies:

    • MLflow Model Registry
    • Custom governance tools
    • Integration with CI/CD systems

5. Inference Services​

The inference services component deploys models for prediction:

  • Serving Patterns:

    • Real-time inference API
    • Batch prediction jobs
    • Edge deployment

  • Optimizations:

    • Model quantization
    • Inference acceleration
    • Request batching

  • Technologies:

    • TensorFlow Serving
    • NVIDIA Triton Inference Server
    • Custom containerized services
    • gRPC/REST APIs

6. Monitoring and Feedback​

The monitoring and feedback system ensures models perform as expected:

  • Performance Monitoring:

    • Prediction accuracy metrics
    • Latency and throughput
    • Resource utilization

  • Data Drift Detection:

    • Input distribution monitoring
    • Feature drift analysis
    • Concept drift detection

  • Feedback Loops:

    • User feedback collection
    • Ground truth acquisition
    • Model retraining triggers

  • Technologies:

    • Prometheus and Grafana for metrics
    • Evidently AI for drift detection
    • Custom feedback collection systems

Data Flow​

Training Flow​

  1. Data is collected from various sources through the ingestion layer
  2. Raw data is processed and transformed into features
  3. Features are stored in the feature store
  4. Training pipeline fetches features and trains models
  5. Models are evaluated and registered in the model registry
  6. Approved models are promoted to production

Inference Flow​

  1. Client applications send requests to the inference service
  2. Inference service loads the appropriate model version
  3. Features are retrieved or computed in real-time
  4. Model generates predictions
  5. Predictions are returned to the client
  6. Prediction logs are stored for monitoring and feedback

Infrastructure​

Our AI infrastructure is built on Kubernetes to provide scalability, reliability, and consistency across environments:

  • Environments:

    • Development
    • Staging
    • Production

  • Resource Management:

    • Dynamic resource allocation
    • GPU/TPU node pools
    • Auto-scaling for inference services

  • Deployment:

    • GitOps workflow with ArgoCD
    • Canary deployments for model updates
    • A/B testing capabilities

  • Security:

    • Network isolation
    • RBAC for access control
    • Secret management
    • Model and data encryption

Integration Points​

Our AI system integrates with other parts of our infrastructure:

  • Spotify Project:

    • Provides music recommendations
    • Analyzes user listening patterns
    • Generates personalized playlists

  • Labelling Platform:

    • Receives labelled data for training
    • Provides active learning suggestions
    • Sends model predictions for validation

  • Ticketing Platform:

    • Processes ticket text for classification
    • Suggests responses for common issues
    • Prioritizes tickets based on content

Design Principles​

Our architecture follows these key principles:

  1. Modularity: Components are loosely coupled for independent development and scaling
  2. Reproducibility: All experiments and deployments are version-controlled and reproducible
  3. Observability: Comprehensive monitoring at all stages of the ML lifecycle
  4. Automation: Automated workflows for training, testing, and deployment
  5. Scalability: Designed to handle growing data volumes and model complexity
  6. Compliance: Built with privacy and regulatory requirements in mind

Future Enhancements​

Planned architectural improvements include:

  • Enhanced federated learning capabilities
  • Multi-model serving optimization
  • Automated neural architecture search
  • Improved explainability tools
  • Edge AI deployment framework
  • Reinforcement learning infrastructure