Architecture Guide

ML Pipeline Architecture

Standard ML Pipeline Components

• Data Ingestion: Apache Kafka, Amazon Kinesis
• Data Preprocessing: pandas, NumPy
• Feature Engineering: Scikit-learn, Feature Tools
• Model Training: TensorFlow, PyTorch
• Model Evaluation: Scikit-learn, MLflow
• Model Deployment: TensorFlow Serving, TFX
• Monitoring and Maintenance: Prometheus, Grafana

AI Model Types and Architectures

Transformer-Based Models

Architecture Classifications

• Encoder-Only Transformers: Focus on encoding input sequences for tasks like text classification, sentiment analysis, and anomaly detection. Examples include BERT models optimized for understanding context.
• Decoder-Only Transformers: Generate text sequences by predicting tokens sequentially, ideal for text generation, summarization, and conversational applications. Modern LLMs like GPT series follow this architecture.
• Encoder-Decoder Transformers: Handle sequence-to-sequence tasks like machine translation, text summarization, and question-answering by processing input sequences and generating output sequences.

Deep Learning Architectures

Neural Network Varieties

• Feedforward Neural Networks (FNNs): Process information unidirectionally for classification, regression, and pattern recognition tasks.
• Convolutional Neural Networks (CNNs): Excel at processing grid-like data such as images and videos, utilizing convolutional layers to capture spatial relationships and hierarchical representations.
• Recurrent Neural Networks (RNNs): Process sequential data where order matters, suitable for speech recognition, language translation, and text generation.
• Long Short-Term Memory (LSTM): Address vanishing gradient problems in RNNs, effectively capturing long-term dependencies in sequential data.

Vision-Language Models (VLMs)

Multimodal AI Systems combine computer vision and natural language processing capabilities, featuring:

• Language Encoder: Captures semantic meaning and contextual associations, typically using transformer architecture
• Vision Encoder: Extracts visual properties like colors, shapes, and textures, converting them to vector embeddings

VLM Applications

• Vision-to-Text Models: Image captioning and Visual Question Answering (VQA)
• Text-to-Vision Models: Image generation from textual descriptions
• Cross-Modal Retrieval Models: Connecting visual and textual information

Integration Patterns and Deployment

Microservices Integration Models

AI-Enhanced Microservice Architectures integrate intelligence components including:

Service Integration Patterns

• Model as a Service (MaaS): Treats each AI model as an autonomous service with REST or gRPC APIs
• Data Lake Pattern: Centralizes raw data storage from various sources, mitigating data silos
• API Gateway Integration: Handles request routing and authentication for AI services

Communication Patterns

• Synchronous: REST APIs, gRPC Services, GraphQL Endpoints, WebSocket Streams
• Asynchronous: Event Streaming, Message Queues, Publish-Subscribe Systems, Webhook Notifications

Enterprise AI Architecture Patterns

Mainstream API-Based Integration

• Structured approaches using cloud-hosted, off-the-shelf models accessed through APIs
• Enhanced with Retrieval Augmented Generation (RAG) for enterprise data integration
• Custom data pipelines incorporating organizational context

Deployment Models

• Tuned Models: Automatically deployed to shared public endpoints after tuning completion
• Model Registry Deployment: Manual deployment with guided UI workflows or Jupyter notebooks
• Serverless API Deployment: Consumption without subscription hosting requirements

Model Serving Architectures

Real-time and Batch Serving:

• Unified REST API: Single interface for CRUD operations and querying
• Auto-scaling Infrastructure: Serverless compute automatically adjusts to demand
• AI Functions Integration: Direct SQL access for analytics workflow integration

Security Frameworks and Posture

ML Model Security Threats

Common Attack Vectors

• Adversarial Machine Learning Attacks: Malicious manipulation of model inputs to produce incorrect outputs, such as single-pixel changes causing misclassification.
• Data Poisoning Attacks: Compromising training data to bias model accuracy, requiring insider access or sophisticated infiltration methods.
• Model Extraction: Direct theft of ML models from applications, particularly prevalent in mobile applications where 66% use inadequately protected ML models.

Security Protection Mechanisms

Multi-layered Defense Strategies

• Encryption and Licensing: Strong licensing platforms combined with model encryption prevent extraction and ensure flexible customer deployment while maintaining IP protection.
• Sophisticated Software Protection: Advanced copy protection tools harden applications against reverse engineering, preventing input manipulation and output integrity attacks.
• Usage Control Systems: Licensing mechanisms limiting classifications per timeframe, total usage, and concurrent instances to prevent unauthorized model training.

Security Frameworks

NIST AI Security Framework

Core components:

• Data Privacy and Protection: Encrypted, anonymized, and securely stored data
• Model Integrity: Protection from adversarial attacks during training and deployment
• Bias and Fairness: Diverse datasets and regular fairness audits
• Transparency and Explainability: Clear decision-making explanations
• Compliance and Governance: Industry regulation adherence
• Continuous Monitoring: Real-time threat detection and risk mitigation

Google's Secure AI Framework (SAIF) addresses ML model risk management, security, and privacy concerns through structured risk assessment and mitigation protocols.

MLOps Approach and Automation

MLOps Maturity Levels

Three Levels of Automation

• Level 0 - Manual Process: Experimental data science with manual model training and deployment using tools like Jupyter Notebooks.
• Level 1 - ML Pipeline Automation: Continuous training with automated data/model validation, achieving experimental-operational symmetry through containerized, modular components.
• Level 2 - CI/CD Pipeline Automation: Complete automation including source control, testing, deployment services, model registry, feature store, and ML metadata management.

MLOps Workflow Components

Essential MLOps Infrastructure

• Source Control: Versioning code, data, and ML model artifacts
• Test & Build Services: Quality assurance and package building for pipelines
• Deployment Services: CD tools for target environment deployment
• Model Registry: Storage for trained ML models
• Feature Store: Preprocessing and feature serving for training and inference
• ML Metadata Store: Tracking training metadata including parameters and metrics

Continuous Operations

Automated ML Lifecycle

• Development & Experimentation: Source code creation for pipeline components
• Pipeline Continuous Integration: Building and testing pipeline components
• Pipeline Continuous Delivery: Deploying pipelines to target environments
• Automated Triggering: Production pipeline execution based on schedules or triggers
• Model Continuous Delivery: Serving models for prediction via REST APIs
• Monitoring: Performance collection triggering retraining or new experiments

Data Governance Framework

AI Data Governance Pillars

Core Governance Components

• Data Classification: Automated categorization of structured vs. unstructured data, identifying sensitive PII and PHI information for appropriate access controls.
• Data Quality Management: AI-driven real-time monitoring, automated cleansing, and quality metrics tracking to ensure model reliability.
• Data Privacy and Security: AI-based privacy tools for sensitive data detection, dynamic access control adjustments, and continuous security monitoring.
• Compliance and Regulatory Adherence: Automated compliance tracking, audit trail maintenance, and regulatory reporting generation.

Data Lifecycle Management

Comprehensive Data Management

• Data Collection: Web scraping, surveys, sensors, and API integration with quality validation mechanisms.
• Data Processing: Cleaning, transformation, and preparation including duplicate removal, missing value handling, and normalization.
• Data Storage: Secure warehousing, lake storage, and cloud solutions with version control and access management.
• Data Governance: Privacy law compliance (GDPR, CCPA, HIPAA), ethical data practices, and audit trail maintenance.

Data Quality Frameworks

Multi-dimensional Quality Assessment

• Intrinsic Dimensions: Accuracy, completeness, consistency, and validity independent of use cases.
• Extrinsic Dimensions: Relevance, timeliness, and contextual appropriateness specific to AI applications.

Quality Management Pipeline

• Schema Validation: Ensuring data adherence to expected formats
• Statistical Checks: Monitoring distribution changes and summary statistics
• Completeness Verification: Flagging missing or incomplete records
• Anomaly Detection: Real-time identification of unusual patterns

Governance Best Practices

Strategic Implementation

• Clear Success Metrics: Measurable KPIs with business leader involvement in goal definition
• Role Definition: Clear accountability across the data lifecycle with comprehensive audit trails
• Balanced Access Control: Avoiding over-restrictive policies that create operational bottlenecks
• Automated Validation: Real-time data quality checks with automated remediation workflows
• Continuous Monitoring: Performance tracking with adaptive governance controls