Implementation for Model Training and Deployment
Here is a Python example that shows the various components of an MLOps pipeline using common libraries.
Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import joblib
from sklearn.datasets import load_iris
Step 2: Data Ingestion and Preparation
def ingest_data():
iris_data = load_iris()
data = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)
data['target'] = iris_data.target
return data
Step 3: Feature engineering
def engineer_features(data):
features = data.drop('target', axis=1)
target = data['target']
return features, target
Step 4: Model Training
def train_model(features, target):
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(f"Model Accuracy: {accuracy_score(y_test, predictions)}")
return model
Output:
Model Accuracy: 1.0
Step 5: Model Deployment
def deploy_model(model, model_path):
joblib.dump(model, model_path)
print(f"Model saved to {model_path}")
Output:
Model saved to model.pkl
Step 6 : Monitoring
def monitor_model(model, features, target):
predictions = model.predict(features)
accuracy = accuracy_score(target, predictions)
print(f"Model Monitoring Accuracy: {accuracy}")
return accuracy
# Example usage
data = ingest_data()
features, target = engineer_features(data)
model = train_model(features, target)
deploy_model(model, 'model.pkl')
# Monitor model performance with new data (using the same dataset for simplicity)
monitor_model(model, features, target)
Output:
Model Monitoring Accuracy: 1.0
1.0
MLOps Pipeline: Implementing Efficient Machine Learning Operations
In the rapidly evolving world of artificial intelligence and machine learning, the need for efficient and scalable operations has never been more critical. Enter MLOps, a set of practices that combines machine learning (ML) and operations (Ops) to automate and streamline the end-to-end ML lifecycle.
This article delves into the intricacies of the MLOps pipeline, highlighting its importance, components, and real-world applications.
Table of Content
- MLOps Pipeline: Streamlining Machine Learning Operations for Success
- Steps To Build MLops Pipeline
- 1. Data Preparation
- 2. Model Training
- 3. CI/CD and Model Registry
- 4. Deploying Machine Learning Models
- Tools and Technologies for MLOps
- Implementation for Model Training and Deployment
- Strategies for Effective MLOps