MLOps maturity describes how well an organization manages machine learning systems in production.
Google commonly describes MLOps maturity through three levels:
- Level 0 — Manual Process
- Level 1 — ML Pipeline Automation
- Level 2 — CI/CD Pipeline Automation
Each level adds more automation and control.
The goal is not simply to deploy a model. The goal is to build a reliable process for developing, deploying, monitoring, and improving machine learning systems.
Level 0 — Manual ML Workflow
Level 0 represents an organization with little or no MLOps automation.
Data scientists usually perform most tasks manually.
A typical workflow looks like this:
Data Collection
↓
Data Preparation
↓
Model Training
↓
Model Evaluation
↓
Manual Deployment
↓
Manual Monitoring
The model may work well during development. However, moving it into production can require many manual steps.
For example, a data scientist may manually prepare data, train the model, save the model file, and give it to an engineering team.
This process can work for small projects.
However, it becomes difficult as the number of models increases.
Main Characteristics of Level 0
At Level 0, you may see:
- Manual data preparation
- Manual model training
- Manual model validation
- Manual deployment
- Limited monitoring
- Separate development and production processes
- Infrequent model updates
- Limited automation
The team may also have difficulty reproducing older model versions.
For example, someone may ask:
“Which dataset and code produced this model?”
If those details were not tracked properly, answering the question can be difficult.
Challenges at Level 0
Manual workflows create several problems.
First, they are slow.
A team may need several manual steps before releasing a new model.
Second, manual work can introduce errors.
A deployment script, configuration, or data preparation step may be changed accidentally.
Third, models can become outdated.
The data used to train a model may no longer represent current production behavior.
This creates a need for better automation.
Level 1 — ML Pipeline Automation
Level 1 introduces automated machine learning pipelines.
Instead of manually performing every step, the organization creates a repeatable pipeline.
A typical workflow looks like this:
Data
↓
Data Validation
↓
Data Preparation
↓
Model Training
↓
Model Evaluation
↓
Model Validation
↓
Model Deployment
The pipeline can run automatically when new data becomes available.
This is a major improvement over Level 0.
What Changes at Level 1?
The organization starts automating the machine learning workflow.
Automation can include:
- Data preparation
- Feature engineering
- Model training
- Model evaluation
- Model validation
- Model deployment
The goal is to make model training and deployment repeatable.
Instead of asking a person to run every step, the pipeline performs those steps consistently.
Continuous Training
Continuous training becomes an important concept at this level.
A model may need retraining when new data becomes available.
For example:
New Training Data
↓
Automated Pipeline
↓
Train Model
↓
Evaluate Model
↓
Validate Model
↓
Deploy Model
This helps the model adapt to changing data.
However, continuous training does not always mean training the model every minute.
The organization chooses a suitable trigger or schedule.
For example, retraining may happen:
- Daily
- Weekly
- After enough new data arrives
- When model performance drops
- After a significant data change
Why Level 1 Is Important
Level 1 creates a repeatable ML process.
The team can run the same workflow multiple times.
This improves consistency.
It also reduces manual work.
The organization can move from:
“A data scientist manually trains the model.”
to:
“A pipeline automatically trains and validates the model.”
This is a major step toward production-grade MLOps.
Level 2 — CI/CD Pipeline Automation
Level 2 adds stronger automation around both machine learning and software delivery.
At this stage, the organization uses CI/CD practices for ML systems.
CI means Continuous Integration.
CD commonly means Continuous Delivery or Continuous Deployment, depending on the workflow.
The system automatically tests and moves changes through different stages.
A simplified workflow looks like this:
Code / Data / Configuration Changes
↓
Continuous Integration
↓
Automated Tests
↓
ML Pipeline Trigger
↓
Model Training
↓
Model Evaluation
↓
Model Validation
↓
Continuous Delivery
↓
Production Model
↓
Monitoring
↓
Feedback / New Data
↓
Training Pipeline
This creates a much more automated ML development and delivery system.
What Changes at Level 2?
Level 2 connects ML pipelines with software engineering practices.
Automation can cover:
- Code testing
- Pipeline testing
- Model validation
- Data validation
- Model training
- Model deployment
- Infrastructure changes
- Production monitoring
- Retraining workflows
This helps teams release changes more frequently and consistently.
Continuous Integration in MLOps
Continuous Integration means regularly testing changes as developers or data scientists make them.
For example, a developer changes feature-processing code.
The CI system can automatically run tests.
Code Change
↓
Automated Tests
↓
Pipeline Validation
↓
Build / Package
If a test fails, the change does not continue through the pipeline.
This helps catch problems earlier.
Continuous Delivery and Deployment
After a model passes the required checks, the organization can automatically prepare or deploy it.
For example:
Validated Model
↓
Deployment Pipeline
↓
Staging Environment
↓
Production Checks
↓
Production
Some organizations use automatic production deployment.
Others require approval before production release.
The exact process depends on business and risk requirements.
How Monitoring Changes Across MLOps Levels
Monitoring is important at every level.
However, its depth increases as MLOps maturity grows.
At Level 0, monitoring may be limited.
A team might only check whether the application is running.
At Level 1, teams can monitor model and pipeline behavior more systematically.
They may track:
- Model accuracy
- Training results
- Data quality
- Pipeline failures
- Prediction statistics
At Level 2, monitoring becomes part of the automated production lifecycle.
Teams can monitor:
- System performance
- API latency
- Prediction quality
- Data drift
- Feature drift
- Model drift
- Resource usage
- Pipeline failures
The monitoring system can also trigger actions.
For example:
Performance Drops
↓
Monitoring Alert
↓
Pipeline Trigger
↓
New Training Data
↓
Model Retraining
↓
Model Validation
This creates a feedback loop.
What Is Data Drift?
Data drift happens when production data changes over time.
Suppose a model was trained using customer behavior from last year.
Customer behavior may change later.
The production data may then look different from the training data.
This can affect model performance.
MLOps systems can monitor these changes.
If the change becomes significant, the team can investigate or trigger retraining.
What Is Model Drift?
Model drift refers to a decline in model performance as real-world conditions change.
For example, a fraud detection model may perform well initially.
Over time, fraud patterns may change.
The model may then become less effective.
A mature MLOps workflow can detect these changes through monitoring.
The organization can then retrain and validate the model.
How Automation Increases Across the Three Levels
The main progression is from manual work to automated ML operations.
At Level 0:
Human → Human → Human → Human
People perform most workflow steps.
At Level 1:
Human
↓
Automated ML Pipeline
↓
Training → Validation → Deployment
The ML workflow becomes automated.
At Level 2:
Code/Data Change
↓
CI/CD System
↓
ML Pipeline
↓
Testing
↓
Training
↓
Validation
↓
Deployment
↓
Monitoring
↓
Feedback
The entire ML delivery process becomes more connected.
How Continuous Training Evolves
Continuous training also becomes more systematic.
At Level 0, retraining is usually manual.
A data scientist decides when to train the model again.
At Level 1, training can become automated.
New data can trigger the training pipeline.
At Level 2, training can become part of a larger automated lifecycle.
For example:
New Data
↓
Data Validation
↓
Training
↓
Model Evaluation
↓
Model Validation
↓
Deployment
↓
Monitoring
This does not mean every newly trained model should automatically reach production.
Production promotion should still follow appropriate validation and release controls.
How Deployment Changes
Deployment also becomes more mature across the levels.
Level 0
Deployment is mostly manual.
A person may copy the model into a production environment.
Level 1
Deployment becomes part of the ML pipeline.
The pipeline can automatically package and deploy a validated model.
Level 2
Deployment becomes integrated with CI/CD.
Changes can pass through automated tests and controlled release stages.
The organization can also use practices such as:
- Staging environments
- Automated validation
- Rollbacks
- Version control
- Approval gates
- Canary releases
- Deployment monitoring
These practices help make model releases more controlled.
Why Versioning Matters
Mature MLOps systems need to track more than model files.
Teams may need to version:
- Source code
- Training data
- Features
- Model artifacts
- Configuration
- Pipeline definitions
- Dependencies
Suppose a model suddenly performs poorly.
The team needs to know what changed.
Versioning helps answer questions such as:
- Which model is currently running?
- Which code created it?
- Which data trained it?
- Which configuration was used?
- When was it deployed?
This makes troubleshooting easier.
Practical Example: Customer Churn Model
Imagine a company that predicts customer churn.
Level 0
A data scientist manually downloads customer data.
They clean the data and train the model.
They manually evaluate the results.
Then someone manually deploys the model.
Level 1
The organization creates an automated pipeline.
New data enters the pipeline.
The pipeline prepares the data, trains the model, evaluates it, and deploys a valid model.
Level 2
The organization connects the ML pipeline with CI/CD.
Code changes automatically trigger tests.
New data can trigger training.
Model quality is checked automatically.
Validated models move through controlled deployment stages.
Production metrics are monitored.
Performance problems can trigger investigation or retraining.
This creates a continuous ML lifecycle.
The Overall MLOps Maturity Journey
The three levels can be understood as a progression:
Level 0
Manual ML Process
↓
Level 1
Automated ML Pipeline
↓
Level 2
Automated CI/CD for ML
Each level improves repeatability and automation.
The organization moves from manually managing individual models toward managing an automated ML system.
The goal is not simply to reach the highest level.
The appropriate maturity level depends on the organization's:
- Number of ML models
- Deployment frequency
- Business risk
- Team size
- Data complexity
- Regulatory requirements
- Monitoring needs
- Operational requirements
A small experimental project may not need the same automation as a large production ML platform.
Final Thoughts
Google's MLOps maturity framework describes three broad levels of development.
Level 0 uses mostly manual ML workflows.
Level 1 introduces automated ML pipelines for repeatable training and deployment.
Level 2 adds CI/CD automation around the complete ML lifecycle.
As maturity increases, organizations generally move toward:
- More automation
- Repeatable pipelines
- Continuous training
- Stronger validation
- Automated deployment
- Better monitoring
- Version control
- Faster and safer model updates
In simple terms:
Level 0 = Run ML manually.
Level 1 = Automate the ML pipeline.
Level 2 = Automate ML with CI/CD.
The framework helps teams understand how they can move from experimental machine learning toward a more reliable and automated production workflow.