What is MLOps? A Complete Guide to Machine Learning Operations in 2026

Machine learning models that stay in Jupyter notebooks don't generate business value. MLOps — Machine Learning Operations — is the set of practices, tools, and organizational patterns that bridge the gap between ML experimentation and production systems that deliver real impact at scale.

The MLOps Problem: Why 87% of ML Models Never Reach Production

According to Gartner, only 53% of AI projects make it from prototype to production. VentureBeat's analysis is even starker: up to 87% of ML projects never get deployed. The reasons are consistent:

• No reproducibility — experiments can't be reliably recreated
• No versioning — model artifacts, data, and code drift independently
• No monitoring — deployed models degrade silently
• No automation — manual handoffs between data scientists and engineers
• No governance — no audit trail for model decisions

MLOps solves each of these with systematic engineering practices borrowed from DevOps, adapted for the unique challenges of machine learning.

MLOps Defined: More Than Just DevOps for ML

MLOps is the discipline of deploying, monitoring, and managing machine learning models in production. It encompasses:

1. Data management — versioning datasets, validating quality, tracking lineage
2. Experiment tracking — logging parameters, metrics, and artifacts reproducibly
3. ML pipelines — automating the flow from data to trained model
4. Model registry — centralized storage with approval workflows
5. Model serving — deploying models as scalable APIs or batch jobs
6. Monitoring — detecting data drift, model degradation, and anomalies
7. CI/CD for ML — automated testing, validation, and deployment of models
8. Governance — audit trails, explainability, and compliance

The key difference from traditional DevOps: in ML systems, the code is only one dimension. Data and model artifacts are equally important, and they change independently.

Traditional Software:  Code → Build → Test → Deploy
MLOps:                 Data + Code + Model → Train → Validate → Deploy → Monitor → Retrain

The MLOps Maturity Model

Google introduced a widely-adopted MLOps maturity framework with three levels:

Level 0: Manual Process

• Data scientists train models in notebooks
• Manual handoff to engineering for deployment
• No pipeline automation
• No monitoring or retraining

# Level 0: The notebook prototype
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
 
df = pd.read_csv("data.csv")
X_train, X_test, y_train, y_test = train_test_split(df.drop("target", axis=1), df["target"])
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test)}")
# Now what? Email the pickle file to engineering?

Level 1: ML Pipeline Automation

• Automated training pipelines
• Continuous training on new data
• Experiment tracking with MLflow or similar
• Model registry with versioning

# Level 1: Automated pipeline with experiment tracking
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, cross_val_score
 
mlflow.set_experiment("customer-churn-model")
 
with mlflow.start_run():
    # Log parameters
    params = {"n_estimators": 100, "max_depth": 10, "min_samples_split": 5}
    mlflow.log_params(params)
 
    # Train
    model = RandomForestClassifier(**params)
    scores = cross_val_score(model, X_train, y_train, cv=5)
    model.fit(X_train, y_train)
 
    # Log metrics
    mlflow.log_metric("cv_mean_accuracy", scores.mean())
    mlflow.log_metric("cv_std_accuracy", scores.std())
    mlflow.log_metric("test_accuracy", model.score(X_test, y_test))
 
    # Register model
    mlflow.sklearn.log_model(model, "model", registered_model_name="churn-predictor")

Level 2: CI/CD for ML

• Automated testing of data, model, and infrastructure
• Automated deployment with canary/shadow strategies
• Continuous monitoring with automated alerts
• Feature store for consistent feature computation
• Full governance and audit trail

# Level 2: CI/CD pipeline for ML (GitHub Actions)
name: ML Pipeline CI/CD
on:
  push:
    paths: ['models/**', 'features/**', 'pipelines/**']
  schedule:
    - cron: '0 2 * * *'  # Nightly retraining
 
jobs:
  data-validation:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Validate training data
        run: python pipelines/validate_data.py
 
  train:
    needs: data-validation
    runs-on: [self-hosted, gpu]
    steps:
      - name: Train model
        run: python pipelines/train.py --experiment prod-churn
      - name: Run model tests
        run: pytest tests/model/ -v
 
  deploy:
    needs: train
    if: github.ref == 'refs/heads/main'
    steps:
      - name: Deploy canary (10% traffic)
        run: python pipelines/deploy.py --strategy canary --traffic 10
      - name: Monitor canary metrics
        run: python pipelines/monitor_canary.py --duration 30m
      - name: Promote to production
        run: python pipelines/deploy.py --strategy promote

Core MLOps Components

1. Feature Store

A feature store is a centralized repository for ML features that ensures consistency between training and serving. Instead of recomputing features differently in notebooks vs. production, you compute once and share.

# Feature definition with Feast
from feast import Entity, Feature, FeatureView, FileSource, ValueType
from datetime import timedelta
 
customer = Entity(name="customer_id", value_type=ValueType.INT64)
 
customer_features = FeatureView(
    name="customer_features",
    entities=[customer],
    ttl=timedelta(days=1),
    features=[
        Feature(name="total_purchases", dtype=ValueType.INT64),
        Feature(name="avg_order_value", dtype=ValueType.DOUBLE),
        Feature(name="days_since_last_purchase", dtype=ValueType.INT64),
        Feature(name="lifetime_value", dtype=ValueType.DOUBLE),
    ],
    online=True,
    source=FileSource(path="data/customer_features.parquet", timestamp_field="event_timestamp"),
)

2. Experiment Tracking

Every training run should be tracked with its parameters, metrics, code version, data version, and artifacts. This enables reproducibility and fair comparison.

Popular tools: MLflow, Weights & Biases, Neptune, Comet ML.

3. Model Registry

A model registry stores trained models with metadata, approval workflows, and deployment history. It serves as the single source of truth for which model version is in production.

4. ML Pipeline Orchestration

ML pipelines automate the sequence of data ingestion, preprocessing, training, evaluation, and deployment. Tools like Kubeflow, Airflow, and Prefect handle scheduling, retry logic, and DAG management.

5. Model Serving

Serving infrastructure exposes models as APIs (real-time) or batch jobs (offline). Key considerations:

• Latency requirements — real-time (< 100ms) vs. batch (minutes-hours)
• Scaling — auto-scaling based on traffic patterns
• A/B testing — traffic splitting between model versions
• Fallback — graceful degradation when models fail

# Model serving with FastAPI + MLflow
from fastapi import FastAPI
import mlflow.pyfunc
 
app = FastAPI()
model = mlflow.pyfunc.load_model("models:/churn-predictor/Production")
 
@app.post("/predict")
async def predict(features: dict):
    import pandas as pd
    df = pd.DataFrame([features])
    prediction = model.predict(df)
    return {
        "prediction": int(prediction[0]),
        "model_version": model.metadata.run_id,
    }

6. Model Monitoring

Production models degrade over time as data distributions shift. Monitoring detects:

• Data drift — input feature distributions change
• Concept drift — the relationship between features and target changes
• Model performance — accuracy, latency, error rates
• Infrastructure health — memory, CPU, GPU utilization

# Data drift detection with Evidently
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
 
report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=training_data, current_data=production_data)
 
drift_results = report.as_dict()
drifted_features = [
    feature for feature, result in drift_results["metrics"][0]["result"]["drift_by_columns"].items()
    if result["drift_detected"]
]
 
if drifted_features:
    alert(f"Data drift detected in: {', '.join(drifted_features)}")
    trigger_retraining_pipeline()

MLOps Tool Landscape in 2026

The MLOps ecosystem has matured significantly. Here's the current landscape:

| Category | Open Source | Managed | |----------|-----------|---------| | Experiment Tracking | MLflow, DVC | Weights & Biases, Neptune | | Pipeline Orchestration | Kubeflow, Airflow, Prefect | Vertex AI, SageMaker | | Feature Store | Feast, Hopsworks | Tecton, Databricks | | Model Serving | Seldon, KServe, BentoML | SageMaker Endpoints, Vertex AI | | Monitoring | Evidently, NannyML | Arize, WhyLabs | | Data Versioning | DVC, lakeFS | Delta Lake, Databricks |

MLOps vs. DevOps vs. DataOps

| Aspect | DevOps | DataOps | MLOps | |--------|--------|---------|-------| | Primary artifact | Code | Data pipelines | Models + Data + Code | | Testing | Unit/integration | Data quality | Data + Model + Integration | | Deployment | Code releases | Pipeline updates | Model + serving infra | | Monitoring | App metrics | Data freshness/quality | Model performance + drift | | Versioning | Git (code) | Schema versions | Code + Data + Model versions | | Rollback | Previous code version | Previous pipeline | Previous model version |

Getting Started with MLOps

For Teams Starting Fresh

1. Start with experiment tracking — Log every training run with MLflow from day one
2. Version your data — Use DVC or lakeFS alongside Git
3. Automate your pipeline — Even a simple Python script > manual notebook execution
4. Add monitoring early — Don't wait until production is broken
5. Document model decisions — Who trained it, what data, why these hyperparameters

For Teams Scaling Up

1. Implement a feature store — Eliminate training-serving skew
2. Build CI/CD for ML — Automate testing and deployment
3. Add governance — Model cards, approval workflows, audit trails
4. Consider Kubeflow — For Kubernetes-native ML orchestration
5. Plan for EU AI Act compliance — If deploying in Europe

MLOps and Security

ML systems introduce unique security risks beyond traditional software:

• Data poisoning — Attackers corrupt training data to manipulate model behavior
• Model theft — Extraction attacks reverse-engineer proprietary models
• Adversarial inputs — Carefully crafted inputs cause misclassification
• Supply chain attacks — Compromised pre-trained models or libraries

A secure MLOps pipeline includes integrity verification at each stage, access controls on model artifacts, and continuous monitoring for anomalous behavior. See our guide on AI supply chain security for defensive strategies.

MLOps and EU AI Act Compliance

The EU AI Act requires organizations to maintain documentation, implement human oversight, and ensure transparency for AI systems. MLOps practices directly support compliance:

• Experiment tracking → audit trail for model development
• Model registry → version history and approval workflows
• Monitoring → ongoing performance assessment
• Data versioning → data lineage and quality documentation

Organizations deploying AI in Europe should integrate compliance requirements into their MLOps pipeline from day one. Our EU AI Act compliance guide covers the full requirements.

Conclusion

MLOps isn't optional anymore — it's the difference between ML prototypes that impress in demos and ML systems that drive business value in production. Start small (experiment tracking + basic automation), iterate toward maturity, and invest in monitoring before you need it.

The organizations that treat ML engineering with the same rigor as software engineering are the ones shipping reliable, scalable, and secure AI systems. That's what MLOps enables.

Need help building your MLOps infrastructure? DeviDevs specializes in production ML platforms — from pipeline design to deployment and monitoring. Get a free assessment →