Complete MLOps Bootcamp With 10+ End To End ML Projects Course Syllabus

An intensive bootcamp designed to take you from foundational MLOps concepts to deploying and managing machine learning models in production. This course spans over 10 hours of hands-on learning, structured into comprehensive modules covering the full lifecycle of ML systems. Expect to spend 8–12 hours completing all modules, projects, and labs, with a strong emphasis on real-world deployment and automation.

Module 1: Introduction to MLOps & Setup

Estimated time: 0.5 hours

• Overview of MLOps and its role in production ML
• Understanding CI/CD and pipeline automation
• Setting up Docker and Kubernetes environments
• Configuring Python for ML workflows

Module 2: Version Control & Workflow Automation

Estimated time: 0.75 hours

• Implementing GitHub Actions for ML automation
• Automating testing workflows in ML pipelines
• Versioning code with Git
• Data versioning using DVC (Data Version Control)

Module 3: Experiment Tracking with MLflow

Estimated time: 1 hour

• Logging parameters, metrics, and models with MLflow
• Organizing and comparing model training runs
• Storing and retrieving model artifacts
• Setting up MLflow tracking server

Module 4: Model Building & Training Pipelines

Estimated time: 1 hour

• Modularizing ML code for reusability
• Building end-to-end training pipelines
• Training and evaluating machine learning models
• Integrating pipelines with version control

Module 5: API Development with FastAPI

Estimated time: 1 hour

• Creating REST APIs for ML inference
• Designing interactive prediction endpoints
• Validating input and output in FastAPI
• Testing API performance and scalability

Module 6: Dockerizing ML Applications

Estimated time: 0.75 hours

• Containerizing ML applications using Docker
• Writing efficient Dockerfiles for ML models
• Creating reproducible environments
• Building and pushing Docker images

Module 7: Orchestrating Workflows with Kubeflow & Airflow

Estimated time: 1.25 hours

• Building Directed Acyclic Graphs (DAGs) for ML workflows
• Orchestrating training and deployment tasks
• Automating workflows with Kubeflow Pipelines
• Scheduling and monitoring jobs with Apache Airflow

Module 8: CI/CD Pipelines for ML

Estimated time: 1 hour

• Automating model testing and validation
• Integrating GitHub Actions into ML workflows
• Automating packaging and deployment with Docker
• Implementing continuous integration for ML systems

Module 9: Deployment to Cloud & Kubernetes

Estimated time: 1 hour

• Deploying ML models using Kubernetes
• Scaling inference services with FastAPI
• Managing model updates and rollbacks
• Configuring cloud environments for production

Module 10: Monitoring & Model Drift Detection

Estimated time: 1 hour

• Setting up monitoring dashboards for ML systems
• Tracking model performance in production
• Detecting data and concept drift
• Automating retraining pipelines on drift detection

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts and models
• Experience with command line and Git version control

What You'll Be Able to Do After

• Build and manage end-to-end machine learning pipelines
• Deploy models using Docker, Kubernetes, and FastAPI
• Automate CI/CD workflows for ML systems using GitHub Actions
• Track, monitor, and retrain models in production
• Apply industry-standard MLOps practices to real-world projects