MLops Pipeline- Full Implementation Code

Implementation for Model Training and Deployment

The code demonstrates the end-to-end workflow of the MLOps pipeline, encompassing model deployment, monitoring, and data ingestion. Emphasizes the essential roles played by each function in the overall operation of the MLOps pipeline, from data preparation to model deployment and monitoring.

Python

# 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

# Data Ingestion and Preparation
def ingest_data():
    # Load Iris dataset
    url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv'
    data = pd.read_csv(url, header=None)
    data.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'target']
    return data

# Feature Engineering
def engineer_features(data):
    features = data.drop('target', axis=1)
    target = data['target']
    return features, target

# 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

# Model Deployment
def deploy_model(model, model_path):
    joblib.dump(model, model_path)
    print(f"Model saved to {model_path}")

# Monitoring (simple example)
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)

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

MLops Pipeline- Full Implementation Code

MLOps Pipeline: Implementing Efficient Machine Learning Operations

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