MLOps Maturity Assessment Tool: Evaluate Your Organization’s ML Operations

MLOps Maturity Assessment Tool: Evaluate Your Organization’s ML Operations

Reviewed: June 4, 2026

Where does your organization stand in MLOps maturity? This interactive assessment evaluates your ML operations across 6 dimensions and provides a personalized improvement roadmap.

.assessment {
font-family: -apple-system, BlinkMacSystemFont, ‚Segoe UI‘, Roboto, sans-serif;
max-width: 850px;
margin: 0 auto;
padding: 20px;
}
.dimension {
background: #f8f9fa;
border-radius: 12px;
padding: 20px;
margin: 16px 0;
border: 1px solid #e9ecef;
}
.dimension h3 {
margin: 0 0 4px 0;
color: #1a1a2e;
}
.dimension .subtitle {
color: #666;
font-size: 14px;
margin-bottom: 12px;
}
.levels {
display: flex;
gap: 8px;
flex-wrap: wrap;
}
.level-btn {
flex: 1;
min-width: 120px;
padding: 12px 8px;
border: 2px solid #dee2e6;
border-radius: 8px;
background: white;
cursor: pointer;
text-align: center;
font-size: 13px;
transition: all 0.15s;
}
.level-btn:hover {
border-color: #4361ee;
background: #f0f4ff;
}
.level-btn.selected {
border-color: #4361ee;
background: #4361ee;
color: white;
}
.level-btn .num {
font-size: 20px;
font-weight: 700;
display: block;
}
button.submit {
background: linear-gradient(135deg, #4361ee, #3a0ca3);
color: white;
border: none;
padding: 16px 40px;
border-radius: 10px;
font-size: 18px;
cursor: pointer;
margin-top: 24px;
font-weight: 600;
width: 100%;
}
#results {
display: none;
margin-top: 24px;
}
.score-card {
background: white;
border-radius: 12px;
padding: 20px;
margin: 12px 0;
box-shadow: 0 2px 8px rgba(0,0,0,0.06);
}
.score-bar {
height: 12px;
background: #e9ecef;
border-radius: 6px;
overflow: hidden;
margin: 8px 0;
}
.score-fill {
height: 100%;
border-radius: 6px;
transition: width 0.5s ease;
}
.overall-score {
text-align: center;
padding: 30px;
background: linear-gradient(135deg, #4361ee, #3a0ca3);
border-radius: 16px;
color: white;
margin-bottom: 20px;
}
.overall-score .big {
font-size: 56px;
font-weight: 800;
line-height: 1;
}
.recommendation {
border-left: 4px solid #4361ee;
padding: 12px 16px;
margin: 8px 0;
background: #f8f9fa;
border-radius: 0 8px 8px 0;
}
.priority-high { border-left-color: #dc3545; }
.priority-medium { border-left-color: #ffc107; }
.priority-low { border-left-color: #28a745; }

const dimNames = {
experimentation: ‚Experimentation & Development‘,
pipelines: ‚ML Pipelines & Automation‘,
deployment: ‚Model Deployment & Serving‘,
monitoring: ‚Monitoring & Observability‘,
governance: ‚Governance & Compliance‘,
collaboration: ‚Team Collaboration & Knowledge‘
};

const recommendations = {
experimentation: {
1: { text: ‚Implement MLflow or Weights & Biases for experiment tracking. Start logging every training run.‘, priority: ‚high‘ },
2: { text: ‚Add model registry and experiment comparison views. Standardize metric logging across the team.‘, priority: ‚medium‘ },
3: { text: ‚Implement automated experiment pipelines that run hyperparameter sweeps and track results.‘, priority: ‚medium‘ },
4: { text: ‚Explore AutoML capabilities and continuous experimentation frameworks.‘, priority: ‚low‘ }
},
pipelines: {
1: { text: ‚Containerize your training code. Set up a basic pipeline tool (Kubeflow, Airflow, or Prefect).‘, priority: ‚high‘ },
2: { text: ‚Add CI/CD for ML: automated testing, quality gates, and deployment automation.‘, priority: ‚high‘ },
3: { text: ‚Implement automated retraining triggers based on data drift or schedule.‘, priority: ‚medium‘ },
4: { text: ‚Build self-healing pipelines that handle failures and retry automatically.‘, priority: ‚low‘ }
},
deployment: {
1: { text: ‚Containerize your model serving. Deploy behind a REST API with basic health checks.‘, priority: ‚high‘ },
2: { text: ‚Implement canary deployments and A/B testing for model versions.‘, priority: ‚high‘ },
3: { text: ‚Build a multi-model serving platform with shared GPU infrastructure.‘, priority: ‚medium‘ },
4: { text: ‚Implement autonomous model management with automatic rollback capabilities.‘, priority: ‚low‘ }
},
monitoring: {
1: { text: ‚Set up data quality monitoring. Track feature distributions and alert on drift.‘, priority: ‚high‘ },
2: { text: ‚Add model quality monitoring: prediction distribution tracking and ground truth comparison.‘, priority: ‚high‘ },
3: { text: ‚Implement full observability: infrastructure + data + model + business metrics.‘, priority: ‚medium‘ },
4: { text: ‚Build predictive monitoring that forecasts issues before they impact users.‘, priority: ‚low‘ }
},
governance: {
1: { text: ‚Create model cards for every production model. Document purpose, training data, and known limitations.‘, priority: ‚high‘ },
2: { text: ‚Implement a model registry with approval workflows and version control.‘, priority: ‚high‘ },
3: { text: ‚Add automated bias testing and full audit trails for regulatory compliance.‘, priority: ‚medium‘ },
4: { text: ‚Implement automated compliance reporting and regulatory documentation.‘, priority: ‚low‘ }
},
collaboration: {
1: { text: ‚Set up a shared ML platform (even a simple one). Start code review for ML code.‘, priority: ‚high‘ },
2: { text: ‚Implement shared feature stores and standardized project templates.‘, priority: ‚medium‘ },
3: { text: ‚Build cross-functional teams that include ML engineers, data engineers, and domain experts.‘, priority: ‚medium‘ },
4: { text: ‚Create an ML center of excellence and internal model marketplace.‘, priority: ‚low‘ }
}
};

function select(btn) {
const dim = btn.closest(‚.dimension‘);
dim.querySelectorAll(‚.level-btn‘).forEach(b => b.classList.remove(’selected‘));
btn.classList.add(’selected‘);
}

function calculateScore() {
const dims = [‚experimentation‘, ‚pipelines‘, ‚deployment‘, ‚monitoring‘, ‚governance‘, ‚collaboration‘];
const scores = {};
let allAnswered = true;

dims.forEach(dim => {
const selected = document.querySelector(`.dimension[data-dim=“${dim}“] .level-btn.selected`);
if (!selected) {
allAnswered = false;
scores[dim] = 0;
} else {
scores[dim] = parseInt(selected.dataset.level);
}
});

if (!allAnswered) {
alert(‚Please rate all 6 dimensions before calculating your score.‘);
return;
}

const total = Object.values(scores).reduce((a, b) => a + b, 0);
const max = dims.length * 5;
const percentage = Math.round((total / max) * 100);

let maturityLevel = “;
let maturityColor = “;
if (percentage >= 90) { maturityLevel = ‚Level 5: Autonomous‘; maturityColor = ‚#28a745‘; }
else if (percentage >= 70) { maturityLevel = ‚Level 4: Managed‘; maturityColor = ‚#4361ee‘; }
else if (percentage >= 50) { maturityLevel = ‚Level 3: Defined‘; maturityColor = ‚#6f42c1‘; }
else if (percentage >= 30) { maturityLevel = ‚Level 2: Developing‘; maturityColor = ‚#ffc107‘; }
else { maturityLevel = ‚Level 1: Initial‘; maturityColor = ‚#dc3545‘; }

let html = ‚

‚;

// Dimension breakdown
dims.forEach(dim => {
const score = scores[dim];
const pct = (score / 5) * 100;
let barColor = score <= 2 ? '#dc3545' : score <= 3 ? '#ffc107' : '#28a745';
html += '

‚;
});

// Recommendations
html += ‚

🎯 Priority Recommendations

‚;
dims.forEach(dim => {
const score = scores[dim];
if (score < 5) {
const rec = recommendations[dim][score];
if (rec) {
html += '

‚;
}
}
});

document.getElementById(‚results‘).innerHTML = html;
document.getElementById(‚results‘).style.display = ‚block‘;
document.getElementById(‚results‘).scrollIntoView({ behavior: ’smooth‘ });
}