7.3 Tooling & Infrastructure

AI Innovations require robust tooling to operate effectively. The right infrastructure enables self-service, automates governance, and provides visibility into AI product health. This section outlines the essential tooling categories, evaluation criteria, and implementation considerations for building an effective AI Innovation technology stack.

Tools Enable, Not Define

Tools should support the AI Innovation model, not drive it. Organizations often over-invest in tooling before establishing ways of working. Start with process clarity, then select tools that reinforce those processes. The best tool is the one your teams will actually use.

The MLOps Technology Stack

Core MLOps Components

A complete MLOps stack supports the full AI product lifecycle:

Component	Purpose	Example Tools	Key Selection Criteria
Experiment Tracking	Track experiments, hyperparameters, results	MLflow, Weights & Biases, Neptune	Collaboration features, integration depth, UI quality
Feature Store	Manage and serve features consistently	Feast, Tecton, Databricks Feature Store	Online/offline serving, governance, latency
Model Registry	Version and manage model artifacts	MLflow, SageMaker Model Registry, Vertex AI	Versioning, metadata, deployment integration
Training Infrastructure	Compute for model training	Kubernetes, SageMaker, Vertex AI, Ray	Scalability, cost efficiency, GPU support
Model Serving	Deploy and serve model predictions	Seldon, KServe, SageMaker, TensorFlow Serving	Latency, scalability, A/B testing support
Monitoring	Track model performance and drift	Evidently, Fiddler, Arize, WhyLabs	Drift detection, explainability, alerting
Pipeline Orchestration	Automate ML workflows	Kubeflow, Airflow, Prefect, Dagster	ML-native features, debugging, scalability

Reference Architecture

Simplified MLOps Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        DATA LAYER                                │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │Data Lake │  │Data      │  │Feature   │  │Privacy   │        │
│  │          │  │Catalog   │  │Store     │  │Controls  │        │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘        │
└─────────────────────────────────────────────────────────────────┘
                              │
┌─────────────────────────────────────────────────────────────────┐
│                     DEVELOPMENT LAYER                            │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │Notebook  │  │Experiment│  │Model     │  │Code      │        │
│  │Env       │  │Tracking  │  │Registry  │  │Repository│        │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘        │
└─────────────────────────────────────────────────────────────────┘
                              │
┌─────────────────────────────────────────────────────────────────┐
│                      DEPLOYMENT LAYER                            │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │CI/CD     │  │Model     │  │A/B Test  │  │Feature   │        │
│  │Pipeline  │  │Serving   │  │Framework │  │Flags     │        │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘        │
└─────────────────────────────────────────────────────────────────┘
                              │
┌─────────────────────────────────────────────────────────────────┐
│                      MONITORING LAYER                            │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │Model     │  │Drift     │  │Alerting  │  │Dashboard │        │
│  │Metrics   │  │Detection │  │System    │  │& Viz     │        │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘        │
└─────────────────────────────────────────────────────────────────┘

Infrastructure Patterns for Pods

Shared Platform, Pod Namespaces

Central platform team provides infrastructure. Pods get isolated namespaces with self-service provisioning.

Best for: Organizations with platform team capacity

Managed Services First

Use cloud-managed ML services (SageMaker, Vertex AI) to minimize infrastructure overhead for pods.

Best for: Smaller AI organizations, cloud-native

Hybrid Approach

Mix of managed services for common needs and custom infrastructure for differentiated capabilities.

Best for: Mature organizations with specific requirements

Governance & Compliance Tools

Model Card Management

Tools for creating, maintaining, and reviewing Model Cards:

Capability	Requirements	Options
Template Management	Customizable templates, version control	Confluence, Notion, custom wiki, Model Card Toolkit
Automated Population	Pull metrics and metadata from ML pipeline	Custom integration, ML Metadata stores
Review Workflow	Approval workflows, comments, history	GitHub/GitLab, custom approval system
Version History	Track changes over model lifecycle	Git-based systems, document management

Fairness & Bias Assessment

Tools for evaluating and monitoring model fairness:

Pre-deployment Testing

Fairlearn (Microsoft)
AI Fairness 360 (IBM)
What-If Tool (Google)
Aequitas (UChicago)

Production Monitoring

Fiddler AI
Arize AI
Arthur AI
Custom dashboards

CI/CD Integration

Fairness checks in pipeline
Automated threshold validation
Blocking gates on violations
Report generation

Audit Trail & Lineage

Essential for regulatory compliance and debugging:

Data Lineage: Track data from source through feature engineering to model training
Model Lineage: Connect deployed models to training runs, code versions, and data versions
Decision Logging: Record individual predictions with inputs for audit purposes
Change Tracking: Document all changes to models, data, and configurations

Lineage Tool Options

Consider: MLflow (experiment tracking), DVC (data versioning), Pachyderm (data lineage), OpenLineage (open standard), or cloud-native options like SageMaker ML Lineage or Vertex AI Metadata.

Collaboration & Visibility Tools

Pod Communication

Effective pods need efficient communication:

Need	Tool Category	AI Innovation Considerations
Real-time Chat	Slack, Teams, Discord	Pod channels, governance alerts integration, incident channels
Documentation	Confluence, Notion, GitBook	Model Cards, runbooks, decision logs, onboarding docs
Video Meetings	Zoom, Teams, Google Meet	Daily standups, stakeholder syncs, retrospectives
Async Updates	Loom, Slack clips	Demo recordings, status updates, knowledge sharing

Work Management

Track pod work and progress:

Agile/Scrum Tools

Jira, Linear, Asana
Support dual-track (ML experiments + engineering)
Custom fields for AI-specific metadata
Integration with ML tools

OKR Tracking

Lattice, Ally.io, 15Five
Pod-level OKRs visible
Alignment to portfolio goals
Progress tracking and check-ins

Roadmap Tools

ProductBoard, Aha!, Roadmunk
AI product roadmaps
Stakeholder visibility
Dependency tracking

Portfolio Visibility

Tools for AI Council and leadership visibility:

Portfolio Dashboard: Aggregate view of all AI products—status, health, risk
Risk Heat Map: Visual representation of AI risk across the portfolio
Health Scorecards: Automated pod health metrics aggregation
Investment Tracking: Resource allocation and ROI across AI initiatives

Build vs. Buy Decisions

Decision Framework

Factor	Build Custom	Buy/Use Existing
Differentiation	Capability is strategic differentiator	Commodity capability
Fit	No existing tool fits requirements	Good tools exist that meet 80%+ of needs
Maintenance	Have capacity for ongoing maintenance	Prefer vendor to handle updates
Integration	Deep integration with custom systems needed	Standard integrations sufficient
Timeline	Can wait for custom development	Need capability quickly
Cost	Long-term cost savings justify investment	Subscription more economical

Common Build vs. Buy Patterns

Typical Recommendations

Usually Buy: Experiment tracking, basic monitoring, work management, documentation
Usually Build: Custom governance workflows, domain-specific fairness tests, proprietary integrations
Hybrid: Model registry (base tool + custom metadata), serving (platform + custom preprocessing)

Integration Architecture

Whatever tools you select, integration is critical:

Define Integration Points

Map where tools need to exchange data: ML pipeline to governance tools, monitoring to alerting, etc.

Standardize on APIs

Use tools with well-documented APIs. Consider OpenAPI, GraphQL, or industry standards like MLflow's APIs.

Build Integration Layer

Create abstraction layer for tool integrations. Allows swapping tools without disrupting pods.

Maintain Flexibility

Avoid deep lock-in to any single vendor. Use open standards where possible.

Tool Rollout Strategy

Phase 1: Foundation

Core MLOps: experiment tracking, model registry, basic serving. Essential for any AI work.

Phase 2: Governance

Model Card system, fairness testing integration, audit trail. Enables responsible scaling.

Phase 3: Automation

CI/CD for ML, automated monitoring, self-service provisioning. Increases velocity.

Phase 4: Advanced

Feature store, advanced A/B testing, portfolio dashboards. Optimizes at scale.

Tools Follow Culture

The most sophisticated tooling cannot compensate for unclear processes or misaligned incentives. Organizations that succeed with AI Innovations first establish clear ways of working, then select tools that reinforce those patterns. Tools should make the right thing easy and the wrong thing hard. When evaluating tools, always ask: "Does this enable our pods to move faster while staying safe?" If the answer isn't clearly yes, reconsider.