MLOps Architecture Audit Framework

Machine Learning Operations excellence assessment

Table of Contents

MLOps Architecture Audit Framework

Comprehensive Assessment for Machine Learning Operations Excellence

Version 1.0 | 2025

Table of Contents

  1. Executive Summary
  2. MLOps Maturity Model
  3. Assessment Dimensions
  4. Data Pipeline Architecture
  5. Model Development & Training
  6. Model Registry & Versioning
  7. Deployment & Serving
  8. Monitoring & Observability
  9. CI/CD for ML
  10. Experiment Tracking
  11. Feature Store
  12. Platform Assessment
  13. Security & Compliance
  14. Cost Optimization
  15. Implementation Roadmap

Executive Summary

The MLOps Imperative

Machine Learning models are only valuable when they’re reliably deployed, monitored, and maintained in production. 87% of ML projects never make it to production, and those that do often suffer from model drift, performance degradation, and operational challenges.

Why MLOps Matters

Framework Overview

This framework evaluates MLOps maturity across eight critical dimensions:

  1. Data Pipeline - Ingestion, processing, and feature engineering
  2. Model Development - Training, experimentation, and validation
  3. Model Registry - Versioning, metadata, and governance
  4. Deployment - Serving infrastructure and patterns
  5. Monitoring - Performance tracking and drift detection
  6. CI/CD - Automation and testing pipelines
  7. Experiment Tracking - Reproducibility and comparison
  8. Feature Store - Feature management and reuse

Key Deliverables

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MLOps Maturity Model

Level 1: Ad-hoc (Score: 0-20)

Characteristics: - Manual model training and deployment - No version control for models - Scripts on local machines - No monitoring or alerting - Data scientists work in isolation

Typical Signs: - Models in Jupyter notebooks - Manual copying of files - No experiment tracking - Email-based model handoffs - Production issues discovered by users

Level 2: Managed (Score: 21-40)

Characteristics: - Basic version control (Git) - Shared development environment - Manual deployment with documentation - Basic monitoring (system metrics) - Some collaboration between teams

Typical Signs: - Code in repositories - Shared file systems for data - Manual model registry (spreadsheets) - Basic logging implemented - Scheduled retraining

Level 3: Standardized (Score: 41-60)

Characteristics: - Automated training pipelines - Model registry in use - Containerized deployments - Performance monitoring - Defined MLOps processes

Typical Signs: - CI/CD for model training - Docker containers for serving - Centralized experiment tracking - A/B testing capability - Feature engineering pipelines

Level 4: Quantified (Score: 61-80)

Characteristics: - Full automation of ML lifecycle - Advanced monitoring and alerting - Feature store implemented - Model governance framework - Self-service capabilities

Typical Signs: - AutoML capabilities - Real-time model monitoring - Automated retraining triggers - Shadow deployments - Cost tracking per model

Level 5: Optimized (Score: 81-100)

Characteristics: - Continuous optimization - Predictive maintenance of models - Advanced AutoML/NAS - Full observability - Innovation at scale

Typical Signs: - Self-healing pipelines - Automated hyperparameter optimization - Multi-cloud deployments - Real-time feature serving - ML-driven ML operations

Maturity Scoring Matrix

Dimension Weight Level 1 Level 2 Level 3 Level 4 Level 5
Data Pipeline 15% Manual Scripted Automated Orchestrated Intelligent
Model Development 15% Notebooks Scripts Pipelines Platforms AutoML
Model Registry 10% None Manual Basic Advanced Governed
Deployment 15% Manual Scripted Containerized Orchestrated Serverless
Monitoring 15% None Logs Metrics Observability Predictive
CI/CD 10% None Basic Standard Advanced GitOps
Experiments 10% None Local Tracked Compared Optimized
Feature Store 10% None Files Database Platform Real-time

Assessment Dimensions

Core MLOps Capabilities Assessment

Capability Current State Target State Gap Priority
Data Management
Data Versioning ☐ None ☐ Basic ☐ Advanced High/Med/Low
Data Lineage ☐ None ☐ Partial ☐ Complete
Data Quality Monitoring ☐ None ☐ Basic ☐ Automated
Model Development
Experiment Tracking ☐ None ☐ Local ☐ Centralized
Hyperparameter Tuning ☐ Manual ☐ Grid ☐ Bayesian
Distributed Training ☐ None ☐ Basic ☐ Advanced
Model Management
Model Registry ☐ None ☐ Basic ☐ Enterprise
Model Versioning ☐ None ☐ Manual ☐ Automated
Model Governance ☐ None ☐ Basic ☐ Complete
Deployment
Deployment Automation ☐ Manual ☐ Semi ☐ Full
Serving Infrastructure ☐ None ☐ Basic ☐ Scalable
Edge Deployment ☐ None ☐ Basic ☐ Advanced
Monitoring
Performance Monitoring ☐ None ☐ Basic ☐ Real-time
Drift Detection ☐ None ☐ Manual ☐ Automated
Business KPI Tracking ☐ None ☐ Basic ☐ Integrated

Data Pipeline Architecture

Assessment Areas

Data Ingestion

Feature Engineering

Data Storage

Data Pipeline Maturity Checklist

Component Not Implemented Basic Advanced Best-in-Class
Data Ingestion ☐ ☐ ☐ ☐
Data Validation ☐ ☐ ☐ ☐
Feature Engineering ☐ ☐ ☐ ☐
Data Versioning ☐ ☐ ☐ ☐
Pipeline Orchestration ☐ ☐ ☐ ☐
Data Lineage ☐ ☐ ☐ ☐
Quality Monitoring ☐ ☐ ☐ ☐

Model Development & Training

Development Environment Assessment

Infrastructure

Training Patterns

Training Infrastructure Comparison

Aspect On-Premise Cloud Hybrid
Scalability Limited Unlimited Flexible
Cost Model CapEx OpEx Mixed
GPU Access Fixed On-demand Both
Maintenance High Low Medium
Security Full control Shared Complex
Latency Low Variable Optimized

Model Registry & Versioning

Model Registry Requirements

Core Features

Advanced Features

Model Registry Platform Comparison

Platform MLflow Vertex AI SageMaker Azure ML Weights & Biases
Versioning ✓ ✓ ✓ ✓ ✓
Metadata ✓ ✓ ✓ ✓ ✓
Artifacts ✓ ✓ ✓ ✓ ✓
Staging ✓ ✓ ✓ ✓ Limited
APIs REST/Python REST/Python REST/Python REST/Python REST/Python
Cloud Native No GCP AWS Azure No
Open Source Yes No No No No
Cost Free Pay-per-use Pay-per-use Pay-per-use Subscription

Deployment & Serving

Deployment Patterns

Batch Inference

Real-time Inference

Edge Deployment

Serving Infrastructure Assessment

Pattern Complexity Scalability Cost Latency Use When
Batch Low High Low High Daily predictions OK
REST API Medium Medium Medium Low Standard web apps
Streaming High High High Very Low Real-time critical
Edge High Limited Low Ultra Low Privacy/offline required
Embedded Medium N/A None None In-app predictions

Monitoring & Observability

Monitoring Framework

Model Performance

Data Quality

System Health

Monitoring Stack Evaluation

Component Current Tool Gaps Recommended Tool
Metrics Collection Prometheus
Visualization Grafana
Alerting PagerDuty
Logging ELK Stack
Tracing Jaeger
ML Monitoring Evidently AI

CI/CD for ML

ML Pipeline Automation

Continuous Integration

Continuous Delivery

Continuous Training

CI/CD Maturity Assessment

Stage Manual Scripted Automated Intelligent
Data Validation ☐ ☐ ☐ ☐
Model Training ☐ ☐ ☐ ☐
Model Testing ☐ ☐ ☐ ☐
Model Deployment ☐ ☐ ☐ ☐
Performance Monitoring ☐ ☐ ☐ ☐
Rollback ☐ ☐ ☐ ☐

Experiment Tracking

Experiment Management Requirements

Core Capabilities

Advanced Features

Experiment Tracking Tools Comparison

Tool MLflow W&B Neptune Comet TensorBoard
Parameter Tracking ✓ ✓ ✓ ✓ Limited
Metric Logging ✓ ✓ ✓ ✓ ✓
Artifact Storage ✓ ✓ ✓ ✓ Limited
Comparison ✓ ✓ ✓ ✓ Basic
Team Collaboration Basic ✓ ✓ ✓ No
Integration Good Excellent Good Good TensorFlow
Pricing Free Paid Paid Freemium Free

Feature Store

Feature Store Architecture

Components

Capabilities Assessment

Capability Required Nice-to-Have Current State
Feature Discovery ☐ ☐
Feature Versioning ☐ ☐
Offline Serving ☐ ☐
Online Serving ☐ ☐
Feature Monitoring ☐ ☐
Time Travel ☐ ☐
Feature Lineage ☐ ☐

Feature Store Platform Comparison

Platform Feast Tecton AWS Feature Store Vertex AI Feature Store Databricks Feature Store
Open Source Yes No No No No
Offline Store ✓ ✓ ✓ ✓ ✓
Online Store ✓ ✓ ✓ ✓ ✓
Streaming Limited ✓ ✓ ✓ ✓
Multi-Cloud Yes Yes No No No
Complexity Medium Low Medium Low Low
Cost Infrastructure only High Pay-per-use Pay-per-use Pay-per-use

Platform Assessment

End-to-End ML Platforms

Cloud-Native Platforms

Amazon SageMaker - Strengths: AWS integration, comprehensive tools - Weaknesses: Vendor lock-in, complexity - Best for: AWS-heavy organizations

Google Vertex AI - Strengths: AutoML, BigQuery integration - Weaknesses: GCP-only, limited customization - Best for: GCP users, AutoML focus

Azure Machine Learning - Strengths: Enterprise features, Azure integration - Weaknesses: Azure-only, learning curve - Best for: Microsoft enterprises

Open Source Platforms

Kubeflow - Strengths: Kubernetes-native, extensible - Weaknesses: Complex setup, maintenance - Best for: Kubernetes experts

MLflow - Strengths: Simple, cloud-agnostic - Weaknesses: Limited features - Best for: Getting started with MLOps

Metaflow - Strengths: Human-centric, Netflix-proven - Weaknesses: Limited ecosystem - Best for: Data scientists

Platform Selection Matrix

Criteria Weight SageMaker Vertex AI Azure ML Kubeflow MLflow
Ease of Use 20% 3/5 4/5 3/5 2/5 4/5
Features 25% 5/5 5/5 5/5 4/5 3/5
Scalability 20% 5/5 5/5 5/5 5/5 3/5
Cost 15% 2/5 2/5 2/5 4/5 5/5
Flexibility 10% 3/5 3/5 3/5 5/5 4/5
Support 10% 5/5 5/5 5/5 2/5 3/5

Security & Compliance

ML Security Assessment

Model Security

Data Security

Infrastructure Security

Compliance Requirements Matrix

Requirement GDPR HIPAA SOC 2 ISO 27001 Current Status
Data Encryption ✓ ✓ ✓ ✓
Access Logging ✓ ✓ ✓ ✓
Right to Delete ✓
Data Residency ✓ ✓
Audit Trail ✓ ✓ ✓ ✓
Consent Management ✓ ✓

Cost Optimization

ML Cost Breakdown

Development Costs

Production Costs

Cost Optimization Strategies

Strategy Impact Effort Current State Target
Spot Instances High Low
Model Optimization High Medium
Batch Processing Medium Low
Caching Medium Low
Auto-scaling High Medium
Reserved Capacity Medium Low
Model Pruning High High

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

Goals: Establish basic MLOps practices

Key Activities: - Set up version control for ML code - Implement basic experiment tracking - Create containerized training environments - Establish model registry - Define MLOps team structure

Success Metrics: - All ML code in Git - 100% of experiments tracked - First model in registry - Basic documentation created

Phase 2: Standardization (Months 4-6)

Goals: Standardize ML workflows

Key Activities: - Build training pipelines - Implement CI/CD for models - Deploy monitoring dashboard - Create feature engineering pipelines - Establish governance policies

Success Metrics: - 50% models using pipelines - Automated testing implemented - Monitoring alerts configured - Feature reuse > 30%

Phase 3: Automation (Months 7-9)

Goals: Automate ML lifecycle

Key Activities: - Implement automated retraining - Deploy feature store - Set up A/B testing framework - Build drift detection system - Create self-service tools

Success Metrics: - 80% models auto-retrained - Feature store in production - A/B tests running - Drift detected automatically

Phase 4: Optimization (Months 10-12)

Goals: Optimize operations

Key Activities: - Implement AutoML capabilities - Optimize infrastructure costs - Build advanced monitoring - Create MLOps metrics dashboard - Scale to multi-region

Success Metrics: - 30% cost reduction - AutoML in use - <1 hour deployment time - 99.9% model availability

Tool Selection Guide

Decision Framework

Evaluation Criteria

  1. Technical Fit (30%)
    • Feature completeness
    • Integration capabilities
    • Performance requirements
  2. Organizational Fit (25%)
    • Team skills
    • Existing technology stack
    • Support requirements
  3. Cost (20%)
    • License costs
    • Infrastructure costs
    • Operational costs
  4. Scalability (15%)
    • Current scale
    • Future growth
    • Multi-region needs
  5. Risk (10%)
    • Vendor lock-in
    • Community support
    • Maturity
Maturity Level Recommended Stack
Level 1-2 MLflow + Kubernetes + Prometheus
Level 2-3 Kubeflow + Feast + Seldon
Level 3-4 Cloud Platform (SageMaker/Vertex/Azure ML)
Level 4-5 Custom Platform + Best-of-breed tools

Industry-Specific Considerations

Financial Services

Healthcare

Retail/E-commerce

Manufacturing

Risk Assessment

Technical Risks

Risk Probability Impact Mitigation Strategy
Model Drift High High Automated monitoring, retraining
Data Quality Issues High Medium Validation pipelines, monitoring
Infrastructure Failure Medium High Redundancy, disaster recovery
Security Breach Low Very High Encryption, access control, auditing
Skill Gaps High Medium Training, hiring, partnerships
Tool Obsolescence Medium Medium Open standards, abstraction layers

Organizational Risks

Risk Probability Impact Mitigation Strategy
Resistance to Change High High Change management, training
Budget Constraints Medium High Phased approach, ROI demonstration
Talent Retention Medium High Career development, competitive comp
Governance Gaps High Medium Clear policies, regular reviews

Next Steps

Immediate Actions (Week 1-2)

  1. Complete MLOps maturity assessment
  2. Identify critical gaps and quick wins
  3. Form MLOps tiger team
  4. Define success metrics
  5. Create 90-day plan

Short-term Goals (Month 1-3)

  1. Implement basic experiment tracking
  2. Set up model registry
  3. Create first automated pipeline
  4. Deploy monitoring dashboard
  5. Document MLOps processes

Long-term Vision (Year 1)

  1. Achieve Level 3 maturity minimum
  2. Deploy 10+ models to production
  3. Reduce deployment time by 80%
  4. Establish MLOps Center of Excellence
  5. Demonstrate clear ROI

Appendix: Assessment Templates

MLOps Readiness Scorecard

Category Score (1-5) Notes
Data Pipeline
Model Development
Model Registry
Deployment
Monitoring
CI/CD
Experiments
Feature Store
Overall Maturity

Tool Evaluation Template

Tool: _____________
Pros
•
•
•
Cons
•
•
•
Cost
• License:
• Infrastructure:
• Operations:
Decision
☐ Adopt ☐ Trial ☐ Assess ☐ Hold

End of MLOps Architecture Audit Framework

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