Sure, while a full end-to-end example with code would be quite lengthy for this format, I can provide simplified examples for each of the key steps we discussed in the previous tutorial. These examples will use Python and some popular libraries for each task.

1. Setting up the Environment

You can initialize a new Git repository and DVC project using the command line:

# Initialize a Git repository
git init

# Initialize a DVC project
dvc init

2. Data Preparation

Assume you have a CSV file as your raw data. We’ll use pandas to clean and preprocess the data:

import pandas as pd

# Load raw data
df = pd.read_csv('raw_data.csv')

# Preprocess the data
df_clean = df.dropna()  # Drop missing values
df_clean.to_csv('clean_data.csv', index=False)

# Add to DVC
!dvc add clean_data.csv

3. Experimentation and Model Building

Let’s use sklearn for building a simple logistic regression model and mlflow for tracking the experiment:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import mlflow

# Load clean data
df_clean = pd.read_csv('clean_data.csv')

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df_clean.drop('target', axis=1), df_clean['target'], test_size=0.2)

# Start an MLflow experiment
with mlflow.start_run():
    model = LogisticRegression()
    model.fit(X_train, y_train)

    # Log model
    mlflow.sklearn.log_model(model, "model")
    
    # Log metrics
    mlflow.log_metric("accuracy", model.score(X_test, y_test))

4. Testing

You can use pytest to create tests for your data and model:

import pytest
import pandas as pd
from sklearn.linear_model import LogisticRegression

def test_data():
    df = pd.read_csv('clean_data.csv')
    assert not df.isnull().any().any(), "Data contains null values."

def test_model():
    df = pd.read_csv('clean_data.csv')
    model = LogisticRegression()
    model.fit(df.drop('target', axis=1), df['target'])
    assert model.score(df.drop('target', axis=1), df['target']) > 0.8, "Model accuracy is too low."

5. Model Deployment

For model deployment, you can save your model to a file and then load it in your serving code:

# Save model to a file
import joblib
joblib.dump(model, 'model.pkl')

# Load model in serving code
model = joblib.load('model.pkl')

You would then include this model file and the serving code in a Docker image.

6. Monitoring

Prometheus and Grafana are often used for monitoring, but they are typically used outside of your Python code, so we’ll skip the code example for this step.

7. Maintenance and Iteration

If the model performance drops, you would go back to the experimentation stage, adjust your model or data, and then rerun your code.

Always remember, these are simplified examples and real-world scenarios would involve more complex data preprocessing, model training, testing, and deployment steps.