End-to-End MLOps Pipeline: A Comprehensive Project

Last Updated : 11 May, 2026

Machine Learning Operations (MLOps) is a set of practices for deploying and maintaining machine learning models in production. It combines DevOps with machine learning to ensure a scalable and reliable lifecycle from development to deployment.

  • Automate the ML lifecycle.
  • Uses CI/CD for continuous delivery.
  • Ensures smooth deployment and tracks performance.

Building a MLOps Pipeline

This project focuses on building an end to end MLOps pipeline to show how ML systems work in real world scenarios, from data to deployment.

1. Objectives

This project focuses on building an end to end pipeline for predicting student academic risk, covering key stages from data processing to deployment.

end_to_end_mlops_pipeline
MLOps Pipeline
  1. Problem and Data: Define the problem and use a real-world Kaggle dataset.
  2. Model Development: Preprocess data, train models and apply hyperparameter tuning.
  3. Model Evaluation: Evaluate model performance using metrics and validation techniques.
  4. Model Tracking: Track experiments and results using MLflow (local setup).
  5. CI/CD: Automate training and reporting using GitHub Actions and CML.
  6. API: Deploy the model using FastAPI for real-time predictions.
  7. Deployment: Containerize the application using Docker for scalable deployment.

2. Problem Statement

The objective of this project is to predict academic risk in higher education to identify students facing performance challenges. It is based on a real world Kaggle competition making it practical for applying MLOps concepts.

  • Objective: Predict students at risk of poor academic performance.
  • Impact: Enable early intervention and support for students.

3. Description of the Dataset

The dataset comes from a higher education institution and includes student details and academic performance across various programs.

1. Data

  • Enrollment Info: Demographics, academic background and socio economic factors.
  • Performance: Academic results from first and second semesters.

2. Target

  • Three classes: Dropout, Enrolled, Graduate (based on final course outcome).

3. Overview

  • Size: 76,518 rows and 38 columns.
  • Type: Mostly numerical features with encoded categorical variables.

4. Key Insights

  • Imbalance: Target classes are unevenly distributed.
  • Structure: Clean dataset with no major missing values.
  • Usage: Suitable for classification tasks and MLOps pipelines.

You can download the dataset by clicking over here.

4. Data Preprocessing and model building

Data preprocessing prepares the dataset for modeling by ensuring it is clean, consistent and in a machine readable format. These steps help improve model performance and reliability.

Step 1: Import required libraries

We will import libraries like pandas, numpy and scikit learn

Python 
import pandas as pd
import numpy as np

from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from imblearn.over_sampling import SMOTE

Step 2: Load the Dataset

Read the dataset with the correct separator to ensure proper structure.

Python 
df = pd.read_csv("your dataset path", sep=';')
df.head()

Output:

output2
Few columns of dataset

Step 3: Basic Exploration

Understand the structure and data types.

Python 
print(df.shape)
print(df.info())

Output:

output2
shape and info of the dataset

Step 4: Handle Missing Values

Check and confirm missing values.

Python 
df.isnull().sum()

No missing values found, so no further preprocessing required to handle missing values.

Step 5: Drop Irrelevant Features

Remove columns that do not contribute to prediction.

Python 
df = df.drop(columns=['id'], errors='ignore')

Step 6: Separate Features and Target

Split dataset into input (X) and output (y).

Python 
X = df.drop(columns=['Target'])
y = df['Target']

Step 7: Encode Target Variable

Convert target labels into numerical form.

Python 
le = LabelEncoder()
y = le.fit_transform(y)

Step 8: Feature Encoding

  • Convert categorical features into numerical format.
  • One-Hot Encoding (for nominal data like Course)
Python 
X = pd.get_dummies(X, columns=['Course'], drop_first=True)

Step 9: Feature Scaling

Normalize numerical features for better model performance.

Python 
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Step 10: Train-Test Split

Split data for training and evaluation.

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

Step 11: Handle Imbalance data

Class imbalance can be addressed using techniques like SMOTE

Python 
sm = SMOTE()
X_train, y_train = sm.fit_resample(X_train, y_train)

Step 12: Train a Model

Start with a simple and reliable model like Random Forest.

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

Step 13: Make Predictions

Python 
y_pred = model.predict(X_test)

Step 14: Evaluate the Model

Use multiple metrics for better understanding.

Python 
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

Output:

Model-Evaluation
Model Evaluation

5. Hyperparameter Tuning

After training the initial model, the next step is to optimize its performance by tuning hyperparameters. This helps find the best configuration for better accuracy and generalization in predicting student academic risk.

Step 1: Set Up MLflow for Experiment Tracking

MLflow is used to track experiments, compare models and log parameters, metrics and results.

  • mlflow.set_experiment(...) creates a project/experiment to group your runs
Python 
mlflow.set_experiment("Student_Risk_Prediction")

Step 2: Perform Hyperparameter Tuning

Use GridSearchCV to find the best parameters for the model.

Python 
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [10, 20, None],
    'min_samples_split': [2, 5]
}

grid = GridSearchCV(RandomForestClassifier(random_state=42),
                    param_grid,
                    cv=3,
                    scoring='accuracy',
                    n_jobs=-1)

grid.fit(X_train, y_train)

Output:

hyperparameter-tuning2
Hyperparameter tuning

Step 3: Log Results with MLflow

Track best parameters and performance.

Python 
with mlflow.start_run():
    mlflow.log_params(grid.best_params_)
    mlflow.log_metric("best_accuracy", grid.best_score_)
    mlflow.sklearn.log_model(grid.best_estimator_, "model")

6. Model Evaluation

After hyperparameter tuning, the best model is evaluated to ensure it performs well on unseen data. This step validates model performance and prepares it for real world use.

Step 1: Load the Best Model

Load the model selected during hyperparameter tuning.

Python 
best_model = grid.best_estimator_

Step 2: Make Predictions

Use the model to generate predictions on test data.

Python 
y_pred = best_model.predict(X_test)

Step 3: Evaluate Performance

Measure how well the model performs using key metrics.

Python 
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

Output:

output
Output
  • Overall accuracy ~75.7% means moderate performance
  • Class 2 shows best performance, recall = 0.89, F1-score = 0.84
  • Class 1 shows poor performance, precision = 0.48, recall = 0.46
  • Macro F1-score = 0.69 means balanced but slightly uneven across classes
  • Weighted F1-score = 0.75 means better performance on classes with more samples

Step 4: Serialize the trained model

Python 
import joblib

joblib.dump(best_model, "model.pkl")

Download full code from here

7. Continuous Integration and Deployment (CI/CD) with CML

CI/CD automates model training, evaluation, reporting and deployment whenever changes are pushed to the repository. In this project, GitHub Actions and CML are used to track performance and simulate deployment of the student risk prediction model.

Step 1: Workflow Overview

  • Code Checkout: Fetch latest code from repository.
  • Environment Setup: Install Python and dependencies.
  • Training: Train and evaluate the model.
  • Reporting: Generate metrics and plots using CML.
  • Deployment: Build and run Docker container (CD step).
XML 
name: CI/CD using CML

on:
  push:
    branches: [ "main" ]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout Repository
        uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Setup CML
        uses: iterative/setup-cml@v2

      - name: Install Dependencies
        run: |
          pip install -r requirements.txt

      - name: Train and Evaluate Model
        run: |
          python train.py

      - name: Generate CML Report
        env:
          REPO_TOKEN: ${{ secrets.CML_TOKEN }}
        run: |
          echo "# Student Risk Prediction Results" >> report.md
          echo "## Model Performance Metrics" >> report.md
          echo "![](./plots/results.png)" >> report.md
          cml comment create report.md

      - name: Build Docker Image
        run: |
          docker build -t student-risk-api .

      - name: Run Docker Container
        run: |
          docker run -d -p 8000:8000 student-risk-api

8. Model Deployment with FastAPI

After training and evaluating the model, the final step is deployment to enable real time predictions. FastAPI is used to build a high performance API for the student risk prediction model.

Step 1: Initialize FastAPI App

Initialize the application and serve static files for the frontend.

Python 
from fastapi import FastAPI
import pandas as pd
import uvicorn

app = FastAPI()

Step 2: Load Trained Model

Python 
import joblib

model_pipe = joblib.load("model.pkl")

Step 3: Define Prediction Endpoint

This endpoint accepts input data and returns predicted student risk.

Python 
from fastapi import HTTPException

@app.post("/predict")
def predict(data: dict):
    try:
        df = pd.DataFrame([data])
        prediction = model_pipe.predict(df)
        return {"prediction": prediction.tolist()[0]}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Step 4: Run the API

Python 
if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=8000)

Step 5: Test the API

  • Open: http://127.0.0.1:8000/docs
  • Use Swagger UI to send input and get predictions

9. Dockerization

Docker is used to containerize the FastAPI application, making the model portable, consistent and easy to deploy across environments.

Step 1: Dockerfile Configuration

The Dockerfile defines the environment and dependencies required to run the API.

XML 
FROM python:3.9-slim

WORKDIR /app

COPY app.py /app/
COPY model.pkl /app/
COPY requirements.txt /app/
COPY static/ /app/static/

RUN pip install --no-cache-dir -r requirements.txt

EXPOSE 8000

CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

Step 2: Build and Run Container

XML 
docker build -t academic-success-predictor .
docker run -p 8000:8000 academic-success-predictor

Step 3: Live Application Output

  • The container runs successfully and serves the application on localhost:8000.
  • Users can input student details through the interface, and the model returns predictions in real time (e.g., predicted academic success class shown after submission).

Step 4: Logs and API Activity

Docker logs confirm:

  • Server startup using Uvicorn
  • API requests (POST /predictions) returning status 200 OK
  • Successful model inference without errors
output-min
Output after running docker Image
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