⚙️ MLOps & Real-World Projects

📘 Introduction

MLOps (Machine Learning Operations) combines machine learning, DevOps, and data engineering practices to manage the entire ML lifecycle — from development to deployment and maintenance.
It ensures reproducibility, scalability, and continuous improvement of machine learning systems in production.

Why MLOps Matters

• Streamlines collaboration between data scientists and engineers
• Ensures consistent model deployment and monitoring
• Automates the retraining and versioning process

🧾 Versioning Data and Models

Concept

Versioning in MLOps involves tracking changes in datasets, code, and models over time — just like version control in software engineering.
This ensures reproducibility and makes it easy to roll back or compare model versions.

Key Components of Versioning

1. Data Versioning – Track dataset changes to ensure model reproducibility.
2. Model Versioning – Manage multiple iterations of trained models.
3. Experiment Tracking – Log hyperparameters, metrics, and results for comparison.

Tools for Versioning

Example: Using DVC for Data and Model Versioning

# Initialize DVC in your project
dvc init

# Add a dataset
dvc add data/train.csv

# Track it with Git
git add data/train.csv.dvc .gitignore
git commit -m "Add training dataset"

# Push data to remote storage
dvc remote add -d myremote s3://mybucket/data
dvc push

Example: Tracking Experiments with MLflow

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Start experiment tracking
mlflow.set_experiment("Iris_Classification")

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

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

accuracy = model.score(X_test, y_test)

# Log metrics and model
mlflow.log_param("n_estimators", 100)
mlflow.log_metric("accuracy", accuracy)
mlflow.sklearn.log_model(model, "model")

📊 Monitoring Deployed Models

Concept

Once a model is deployed, it must be monitored continuously to ensure consistent performance and reliability.
Over time, models can degrade due to data drift, concept drift, or changing user behavior.

Key Metrics to Monitor

Tools for Model Monitoring

Example: Monitoring with Evidently AI

from evidently.report import Report
from evidently.metrics import DataDriftPreset

# Compare production vs. training data
reference_data = train_df
current_data = production_df

report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=reference_data, current_data=current_data)

# Generate report
report.save_html("data_drift_report.html")

🔁 Automation Pipelines

Concept

Automation pipelines streamline ML workflows — from data collection to model deployment and monitoring — ensuring consistency and scalability.

These pipelines are the backbone of Continuous Integration (CI) and Continuous Deployment (CD) for machine learning.

Typical MLOps Pipeline

1. Data Ingestion → Collect and preprocess new data
2. Model Training → Retrain with updated data
3. Model Validation → Evaluate metrics and performance
4. Model Deployment → Push updated model to production
5. Monitoring & Feedback → Detect drift and trigger retraining

Tools for Automation Pipelines

Example: Simple Airflow DAG

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def preprocess_data():
    print("Data preprocessing...")

def train_model():
    print("Training model...")

def evaluate_model():
    print("Evaluating model...")

# Define DAG
with DAG("ml_pipeline", start_date=datetime(2023, 1, 1), schedule_interval="@daily", catchup=False) as dag:
    preprocess = PythonOperator(task_id="preprocess", python_callable=preprocess_data)
    train = PythonOperator(task_id="train", python_callable=train_model)
    evaluate = PythonOperator(task_id="evaluate", python_callable=evaluate_model)

    preprocess >> train >> evaluate

Benefits of Automation Pipelines:

• Faster experimentation cycles
• Reduced human errors
• Continuous delivery of improved models

🧠 Summary

MLOps brings discipline and automation to machine learning workflows — enabling scalable, reliable, and maintainable AI systems.
By mastering versioning, monitoring, and automation, you can take your ML projects from experimental notebooks to production-ready systems.