Chapter 16 — Platforms & Architecture

Overview

Define reference architectures that cover data, models, orchestration, and deployment patterns. The platform and architecture decisions you make early in your AI journey will enable or constrain your capabilities for years. A well-designed AI platform provides the foundation for rapid experimentation, reliable production deployments, and continuous improvement.

Why It Matters

Architecture sets the runway for many teams. Standard components reduce duplicated effort and risk. Poor architectural decisions compound over time:

• Technical Debt: Teams build workarounds when platform doesn't meet needs, creating maintenance burden
• Scalability Bottlenecks: Architecture that works for 100 models fails at 1,000 models
• Vendor Lock-in: Tight coupling to specific vendors limits flexibility and increases costs
• Security Gaps: Ad-hoc solutions bypass security controls and create vulnerabilities
• Team Friction: Inconsistent tooling across teams reduces collaboration and knowledge sharing

Real-world impact:

• E-commerce company rebuilt entire ML platform after initial design couldn't scale beyond 50 models
• Healthcare AI startup spent 18 months migrating off tightly-coupled cloud vendor
• Financial services firm saved $2M annually by standardizing on reference architecture

Platform Architecture Layers

Layered Architecture Model

graph TB
    subgraph "Control Plane"
        CP1[Catalog & Metadata]
        CP2[Governance & Compliance]
        CP3[Monitoring & Observability]
        CP4[Security & IAM]
    end

    subgraph "Data Layer"
        D1[Raw Data Lake]
        D2[Curated Data Warehouse]
        D3[Feature Store]
        D4[Vector Database]
    end

    subgraph "Compute Layer"
        C1[Training Clusters]
        C2[Batch Inference]
        C3[Real-time Serving]
        C4[Experimentation]
    end

    subgraph "Orchestration Layer"
        O1[Workflow Engine]
        O2[Event Bus]
        O3[Scheduler]
        O4[Job Queue]
    end

    subgraph "Model Layer"
        M1[Model Registry]
        M2[Experiment Tracking]
        M3[Version Control]
        M4[A/B Testing]
    end

    CP1 & CP2 & CP3 & CP4 -.-> D1 & C1 & O1 & M1
    D1 & D2 & D3 & D4 --> C1 & C2 & C3
    O1 & O2 & O3 & O4 --> C1 & C2 & C3
    C1 --> M1
    M1 --> C2 & C3
    M2 & M3 & M4 --> M1

    style CP1 fill:#f96,stroke:#333,stroke-width:2px
    style D3 fill:#bbf,stroke:#333,stroke-width:2px
    style M1 fill:#9f9,stroke:#333,stroke-width:2px

Platform vs. Product Teams

Data Architecture Patterns

Lakehouse Architecture

graph LR
    subgraph "Ingestion"
        I1[Batch Sources]
        I2[Streaming Sources]
        I3[API Sources]
    end

    subgraph "Bronze Layer - Raw"
        B1[Raw Data Lake<br/>Parquet/Delta]
        B2[Change Data Capture]
        B3[Audit Logs]
    end

    subgraph "Silver Layer - Curated"
        S1[Cleaned Data]
        S2[Deduplicated]
        S3[Standardized Schema]
    end

    subgraph "Gold Layer - Business"
        G1[Aggregated Features]
        G2[Business Metrics]
        G3[ML-Ready Datasets]
    end

    subgraph "Serving Layer"
        SV1[Analytics Warehouse]
        SV2[Feature Store]
        SV3[Vector Store]
    end

    I1 & I2 & I3 --> B1 & B2 & B3
    B1 & B2 & B3 --> S1 & S2 & S3
    S1 & S2 & S3 --> G1 & G2 & G3
    G1 & G2 & G3 --> SV1 & SV2 & SV3

    style G3 fill:#bbf,stroke:#333,stroke-width:2px
    style SV2 fill:#f96,stroke:#333,stroke-width:2px

Feature Store Architecture

graph TB
    subgraph "Feature Definition"
        FD1[Feature Registry]
        FD2[Feature Metadata]
        FD3[Lineage Tracking]
    end

    subgraph "Offline Store"
        OS1[Historical Features<br/>Data Warehouse]
        OS2[Point-in-Time Correct]
        OS3[Training Set Generation]
    end

    subgraph "Online Store"
        ON1[Real-time Features<br/>Redis/DynamoDB]
        ON2[Low-latency Lookup]
        ON3[< 10ms Response]
    end

    subgraph "Feature Computation"
        FC1[Batch Pipeline<br/>Daily/Hourly]
        FC2[Stream Pipeline<br/>Real-time]
        FC3[On-Demand<br/>Request-time]
    end

    FD1 & FD2 & FD3 --> OS1 & ON1
    FC1 --> OS1 & ON1
    FC2 --> ON1
    OS1 --> OS3
    ON1 --> ON2

    OS3 -.-> Training[Model Training]
    ON2 -.-> Inference[Model Inference]

    style ON1 fill:#f96,stroke:#333,stroke-width:2px
    style OS3 fill:#bbf,stroke:#333,stroke-width:2px

Technology Selection Matrix

Data Storage Comparison

Model Serving Patterns

graph TB
    subgraph "Batch Serving"
        B1[Scheduled Job] --> B2[Load Full Dataset]
        B2 --> B3[Batch Inference]
        B3 --> B4[Write to Database/S3]
    end

    subgraph "Real-time Serving"
        R1[API Request] --> R2[Feature Lookup]
        R2 --> R3[Model Inference]
        R3 --> R4[Response < 100ms]
    end

    subgraph "Streaming Serving"
        S1[Event Stream] --> S2[Feature Enrichment]
        S2 --> S3[Model Inference]
        S3 --> S4[Sink to Target]
    end

    subgraph "Edge Serving"
        E1[Mobile/IoT Device] --> E2[Local Model]
        E2 --> E3[On-Device Inference]
        E3 --> E4[Offline Capable]
    end

    style R3 fill:#f96,stroke:#333,stroke-width:2px
    style S3 fill:#bbf,stroke:#333,stroke-width:2px

Orchestration Tool Comparison

ML Training Infrastructure

Distributed Training Architecture

graph TB
    subgraph "Training Orchestrator"
        TO1[Experiment Tracking]
        TO2[Hyperparameter Tuning]
        TO3[Resource Allocation]
    end

    subgraph "Compute Cluster"
        C1[GPU Node 1<br/>8x A100]
        C2[GPU Node 2<br/>8x A100]
        C3[GPU Node 3<br/>8x A100]
        C4[GPU Node 4<br/>8x A100]
    end

    subgraph "Data Pipeline"
        DP1[Data Loader]
        DP2[Preprocessing]
        DP3[Distributed Cache]
    end

    subgraph "Model Registry"
        MR1[Checkpoints]
        MR2[Versioned Models]
        MR3[Metadata]
    end

    TO1 & TO2 & TO3 --> C1 & C2 & C3 & C4
    DP1 --> DP2 --> DP3
    DP3 --> C1 & C2 & C3 & C4
    C1 & C2 & C3 & C4 --> MR1 & MR2 & MR3

    style C1 fill:#f96,stroke:#333,stroke-width:2px
    style MR2 fill:#bbf,stroke:#333,stroke-width:2px

Compute Options Comparison

Build vs. Buy Decision Framework

Component-by-Component Analysis

✅ = Recommended | ⚠️ = Situational | ❌ = Avoid

Real-World Case Study: E-commerce ML Platform

Challenge

Growing e-commerce company had 5 data science teams building 50+ models with inconsistent tooling. No standard deployment process, models breaking in production, 6-week deployment cycles.

Solution Architecture

Built centralized ML platform over 6 months:

Data Layer:

• S3 data lake (Bronze/Silver/Gold layers)
• Snowflake warehouse for analytics
• Feast feature store for online/offline features

Compute:

• EKS cluster for distributed training
• SageMaker for model serving
• Spot instances for 70% cost savings

Orchestration:

• Airflow for data pipelines
• Argo Workflows for ML pipelines
• Event-driven triggers via EventBridge

Observability:

• Datadog for infrastructure metrics
• Custom model monitoring with Grafana
• PagerDuty for alerting

Implementation Timeline

Results After 12 Months

Key Success Factors

1. Executive Sponsorship: Dedicated budget and platform team
2. Golden Paths: Reference implementations for common patterns
3. Migration Support: Office hours and hands-on help
4. Iterative Rollout: Start with one team, expand gradually
5. Clear Metrics: Track adoption, performance, cost
6. Documentation: Comprehensive guides and examples

Implementation Checklist

Foundation Phase (Week 1-4)

□ Define reference architecture diagram
□ Select core technologies (lakehouse, warehouse, orchestration)
□ Establish infrastructure-as-code practices (Terraform, CDK)
□ Set up networking and security (VPCs, security groups, IAM)
□ Deploy monitoring and logging infrastructure
□ Create developer environments (sandbox, dev, staging, prod)

Platform Services (Week 5-12)

□ Deploy data lake/lakehouse with governance
□ Set up data warehouse and access patterns
□ Implement feature store (if needed)
□ Deploy experiment tracking system (MLflow)
□ Set up model registry
□ Configure CI/CD pipelines for ML workflows
□ Implement model serving infrastructure
□ Deploy orchestration platform (Airflow)

Developer Experience (Week 13-16)

□ Create golden path templates and examples
□ Write platform documentation and runbooks
□ Set up developer onboarding guides
□ Create self-service workflows (request access, deploy model)
□ Implement platform SDK/CLI
□ Establish office hours and support channels

Governance & Operations (Ongoing)

□ Define cost allocation and chargeback
□ Implement usage quotas and rate limits
□ Set up audit logging and compliance reporting
□ Create disaster recovery and backup procedures
□ Establish SLAs for platform services
□ Schedule regular capacity planning reviews

Best Practices

1. Start Simple, Evolve: Begin with proven tools, add complexity only when needed
2. Design for Portability: Avoid vendor lock-in where possible; use open formats and standards
3. Automate Everything: Infrastructure, deployments, testing, monitoring
4. Measure Platform Adoption: Track usage metrics, gather feedback, iterate
5. Invest in Developer Experience: Platform is only valuable if teams use it
6. Plan for Scale: Design for 10x current scale, implement for current + 3x
7. Security from Day One: Easier to build in than bolt on later
8. Document Decisions: Maintain Architecture Decision Records (ADRs)

Common Pitfalls

1. Over-Engineering: Building for Google-scale when you have 10 models
2. Under-Engineering: Not planning for growth, hitting scalability walls
3. Tool Sprawl: Too many overlapping tools, fragmented ecosystem
4. Neglecting DX: Powerful platform nobody can use
5. Ignoring Costs: Building without cost constraints, surprise bills later
6. Tight Coupling: Hard dependencies make changes difficult
7. No Governance: Platform without guard rails leads to chaos
8. Premature Optimization: Optimizing before understanding bottlenecks