MLOps Masterclass

This masterclass is designed to teach end-to-end MLOps, from building a machine learning model to deploying and managing it in a real production environment.

The focus is on hands-on learning, real tools, and industry practices.

Module 1: MLOps Foundations

• What is MLOps and why it matters

• ML lifecycle in real companies

• Difference between:

  • Data Science

  • DevOps

  • MLOps

• Common challenges in production ML systems

Module 2: Python for ML and Automation

• Python fundamentals for ML workflows

• Data handling using:

  • Pandas

  • NumPy

• Writing automation scripts

• Data preprocessing and feature engineering

Hands-on:
Build a simple ML model using Python.

Module 3: Machine Learning Model Development

• Supervised learning concepts

• Training models using Scikit-learn

• Model evaluation techniques

• Saving and loading models

Project:
Train and evaluate a real-world ML model.

Module 4: ML Project Structure and Versioning

• Structuring ML projects properly

• Git for ML workflows

• Data and model versioning concepts

• Introduction to tools like DVC

Module 5: Building Model APIs

• Converting ML models into services

• Building REST APIs using:

  • FastAPI or Flask

• Testing and validating predictions

Hands-on:
Expose the trained model as a REST API.

Module 6: Containerization with Docker

• Docker basics

• Writing Dockerfiles

• Containerizing the ML application

• Running containers locally

Project:
Deploy the model inside a Docker container.

Module 7: CI/CD for MLOps

• CI/CD concepts for ML

• Differences between software and ML pipelines

• Automating:

  • Model tests

  • Build processes

  • Deployments

• Using:

  • GitHub Actions or Azure DevOps

Hands-on:
Create a CI/CD pipeline for the ML model.

Module 8: Cloud Deployment

• Deployment strategies for ML models

• Deploying to cloud platforms (Azure preferred)

• Running containerized models in the cloud

• Basic scaling concepts

Project:
Deploy your ML model to the cloud.

Module 9: Monitoring and Observability

• Logging predictions

• Monitoring model performance

• Data drift and model drift concepts

• Alerting and reliability basics

Module 10: End-to-End Capstone Project

Students build a complete production-ready ML system:

1. Data preprocessing

2. Model training

3. Model versioning

4. API creation

5. Docker container

6. CI/CD pipeline

7. Cloud deployment

8. Basic monitoring setup

Tools You Will Work With

• Python

• Scikit-learn

• Git & GitHub

• FastAPI or Flask

• Docker

• GitHub Actions or Azure DevOps

• Azure Cloud

What Makes This a Masterclass

• Real-world projects

• End-to-end pipeline

• DevOps + ML integration

• Interview preparation

• Resume and career guidance

Who This Masterclass Is For

• Python developers

• DevOps engineers

• Data science beginners

• IT professionals moving into AI roles

Final Outcome

By the end of the masterclass, you will be able to:

• Build a machine learning model

• Convert it into a production API

• Containerize it using Docker

• Create a CI/CD pipeline

• Deploy it to the cloud

• Monitor it in production