Deploying Machine Studying Fashions: A Step-by-Step Tutorial

import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler

# Load your knowledge
df = pd.read_csv('your_data.csv')

# Deal with lacking values
imputer_mean = SimpleImputer(technique='imply')
df['numeric_column'] = imputer_mean.fit_transform(df[['numeric_column']])

# Encode categorical variables
one_hot_encoder = OneHotEncoder()
encoded_features = one_hot_encoder.fit_transform(df[['categorical_column']]).toarray()
encoded_df = pd.DataFrame(encoded_features, columns=one_hot_encoder.get_feature_names_out(['categorical_column']))

# Normalize and standardize numerical options
# Standardization (zero imply, unit variance)
scaler = StandardScaler()
df['standardized_column'] = scaler.fit_transform(df[['numeric_column']])

# Normalization (scaling to a spread of [0, 1])
normalizer = MinMaxScaler()
df['normalized_column'] = normalizer.fit_transform(df[['numeric_column']])

import pandas as pd
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler

# Load your knowledge
df = pd.read_csv('knowledge.csv')

# Cut up knowledge into coaching and testing units
X = df.drop(columns=['target_column'])
y = df['target_column']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Hyperparameter tuning
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10]
}

grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy',
                           n_jobs=-1)

# Match the grid search to the info
grid_search.match(X_train, y_train)

# Get the very best mannequin from the grid search
best_model = grid_search.best_estimator_

# Cross-validation to evaluate mannequin generalization and robustness
cv_scores = cross_val_score(best_model, X_train, y_train, cv=5, scoring='accuracy')

print(f"Cross-validation scores: {cv_scores}")
print(f"Mean cross-validation score: {cv_scores.mean()}")

import joblib

joblib.dump(mannequin, 'mannequin.pkl')

# Docker code
FROM python:3.8-slim
COPY mannequin.pkl /app/mannequin.pkl
COPY app.py /app/app.py
WORKDIR /app
RUN pip set up -r necessities.txt
CMD ["python", "app.py"]

aws ec2 run-instances 
    --image-id ami-0abcdef1234567890 
    --count 1 
    --instance-type t2.micro 
    --key-name MyKeyPair 
    --security-group-ids sg-0abcdef1234567890 
    --subnet-id subnet-0abcdef1234567890

az vm create 
  --resource-group myResourceGroup 
  --name myVM 
  --image UbuntuLTS 
  --admin-username azureuser 
  --generate-ssh-keys

gcloud compute situations create my-instance 
  --zone=us-central1-a 
  --machine-type=e2-medium 
  --subnet=default 
  --network-tier=PREMIUM 
  --maintenance-policy=MIGRATE 
  --image=debian-9-stretch-v20200902 
  --image-project=debian-cloud 
  --boot-disk-size=10GB 
  --boot-disk-type=pd-standard 
  --boot-disk-device-name=my-instance

# Utilizing Jenkins for CI/CD pipeline
pipeline {
  agent any
  levels {
    stage('Construct') {
      steps {
        sh 'python setup.py construct'
      }
    }
    stage('Take a look at') {
      steps {
        sh 'python -m unittest uncover'
      }
    }
    stage('Deploy') {
      steps {
        sh 'docker construct -t mymodel:newest .'
        sh 'docker run -d -p 5000:5000 mymodel:newest'
      }
    }
  }
}

# Import obligatory libraries
from sklearn.metrics import accuracy_score, precision_score, recall_score

# Load your check knowledge 
test_df = pd.read_csv('your_test_data.csv')  

X_test = test_df.drop(columns=['target_column'])
y_test = test_df['target_column']

# Predict outcomes on the check set
y_pred_test = best_model.predict(X_test)

# Consider efficiency metrics
test_accuracy = accuracy_score(y_test, y_pred_test)
test_precision = precision_score(y_test, y_pred_test, common="weighted")
test_recall = recall_score(y_test, y_pred_test, common="weighted")

# Print efficiency metrics
print(f"Test Set Accuracy: {test_accuracy}")
print(f"Test Set Precision: {test_precision}")
print(f"Test Set Recall: {test_recall}")

aws cloudwatch put-metric-alarm --alarm-name CPUAlarm --metric-name CPUUtilization 
--namespace AWS/EC2 --statistic Common --period 300 --threshold 70 
--comparison-operator GreaterThanThreshold --dimensions "Name=InstanceId,Value=i-1234567890abcdef0" 
--evaluation-periods 2 --alarm-actions arn:aws:sns:us-east-1:123456789012:my-sns-topic

az monitor metrics alert create --name 'CPU Alert' --resource-group myResourceGroup 
--scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/suppliers/Microsoft.Compute/virtualMachines/{vm-name} 
--condition "avg Percentage CPU > 80" --description 'Alert if CPU utilization exceeds 80%'