Master MLOps Fundamentals & Workflow

Chapter 00 – Part 1 : Install Python and Set Up a Virtual Environment

Chapter 00 – Part 2 : Install Git and Configure GitHub

Chapter 00 – Part 3 : Install VS Code, Jupyter, and Verify the Full Setup

Quiz Chapter 00

Chapter 01 – Part 1 : What is MLOps and Why Does It Matter?

Chapter 01 – Part 2 : The MLOps Landscape: Tools and Ecosystem

Chapter 01 – Part 3 : Your First MLOps Pipeline: From Notebook to Script

Chapter 02 – Part 1 : What is DVC and Why Do Datasets Need Versioning?

Chapter 02 – Part 2 : Initialize DVC and Track Your First Dataset

Chapter 02 – Part 3 : DVC Pipelines and Exercises

Chapter 03 – Part 1 : What is MLflow and Why Track Experiments?

Chapter 03 – Part 2 : Log Your First Experiment with MLflow

Chapter 03 – Part 3 : MLflow Model Registry and Production Promotion

Chapter 04 – Part 1 : What is FastAPI and Why Use It for Model Serving?

Chapter 04 – Part 2 : Build the Wine Quality Prediction API

Chapter 04 – Part 3 : Environment Variables, Batch Prediction, and API Exercises

Chapter 05 – Part 1 : Dockerfile for ML APIs — Build and Run

Chapter 05 – Part 2 : Docker Compose for the Full MLOps Stack

Chapter 05 – Part 3 : Push to Docker Hub and Container Exercises

Chapter 06 – Part 1 : CI/CD for ML — What and Why

Chapter 06 – Part 2 : Build the GitHub Actions ML Pipeline

Chapter 06 – Part 3 : Automated Retraining and CI/CD Exercises

Chapter 07 – Part 1 : Why Monitor ML Models? Data Drift and Model Decay

Chapter 07 – Part 2 : Building a Monitoring Dashboard with Evidently

Chapter 07 – Part 3 : Monitoring Best Practices and Exercises

Chapter 08 – Part 1 : What is Apache Airflow and Why Use It for ML?

Chapter 08 – Part 2 : Write Your First ML Pipeline DAG

Chapter 08 – Part 3 : Airflow Best Practices and Exercises

Chapter 09 – Part 1 : Complete MLOps Project Architecture and Planning

Chapter 09 – Part 2 : Data Versioning and Experiments for the Churn Project

Chapter 09 – Part 3 : Churn Prediction API, Docker Compose, and CI/CD

Chapter 09 – Part 4 : Airflow DAG, Complete Review, and Project Deployment

Chapter 10 – Part 1 : MLOps Best Practices: Security and Code Quality

Chapter 10 – Part 2 : Cloud Deployment — AWS EC2 with Docker

Chapter 10 – Part 3 : MLOps at Scale: Kubernetes Introduction and Next Steps

Chapter 10 – Part 4 : Final Quiz and Complete Course Recap

Evaluation 01 — Practical Quiz: MLOps Foundations

Evaluation 02 — Midterm Exam: FastAPI, Docker, and CI/CD

Evaluation 03 — Final Exam: All Chapters

Final Project — Complete Guide and Solution Walkthrough

Final Project: End-to-End MLOps Pipeline

Lab 01 – Installing MLflow on an Ubuntu 22.04 VM

Lab 02 – What is MLflow?

Lab 03 – First MLflow Experiment with ElasticNet

Lab 04 – MLflow Experiment with a requirements.txt File

Lab 05 – MLflow Experiment with set_tracking_uri and get_tracking_uri

Lab 06 – Create an MLflow Experiment with create_experiment

Lab 07 – MLflow Experiment with active_run and last_active_run

Lab 08 – MLflow Experiment with log_artifacts

Lab 09 – Using mlflow.set_tags in an Experiment

Lab 10 – Running Multiple Runs in a Single MLflow Script

Lab 11 – Multiple Experiments with Different Algorithms

Lab 12 – Using mlflow.autolog

Lab 13 – MLflow with PostgreSQL and S3 as Backend

Lab 14 – Model Signature and Input Examples in MLflow

Lab 15 – MLflow Pyfunc with Scikit-learn Wrapper, Joblib and Conda

Lab 16 – MLflow Pyfunc: complete version with tracking URI and run information

Lab 17 – Loading and evaluating a Pyfunc model after deployment

Lab 18 – Automated evaluation with mlflow.evaluate

Lab 19 – Advanced evaluation with custom metrics and visual artifacts

Lab 20 – Comparative evaluation with baseline model and validation thresholds

Lab 21 – Registering an ElasticNet Model with registered_model_name

Lab 22 – Tracking and Registration with Path Validation and Pickle

Lab 23 – Registering and Loading a Versioned Model with MLflow

Lab 24 – Registering an External Model in MLflow with cloudpickle

Lab 25 – MLflow Experiment with a main() Function

Lab 26 – Running an MLflow Project with mlflow.projects.run

Lab 27 – Advanced CLI Commands for Complete MLflow Management

Lab 28 – Annotated Guide to MLflow CLI Commands

Lab 29 – Reference Summary: All Labs at a Glance

Lab 30 – Progressive Code Comparison: Scripts 1 to 6 Explained