In this module, you will be introduced to MLOps, its core principles, and its importance in modern machine learning workflows. The evolution from traditional MLOps to emerging paradigms like LLMOps and AgenticAIOps will be covered. You'll also compare DevOps and MLOps, examining their similarities and differences, and explore the growing role of the MLOps Engineer.

In this module, you will set up the environment and tools necessary to work on the house price prediction project. You'll get hands-on experience in setting up Docker containers, configuring MLflow for experiment tracking, and creating isolated Python virtual environments for reproducibility. Additionally, you'll understand the end-to-end ML lifecycle and how MLOps practices integrate into it.

This module focuses on preparing and transforming raw data for modeling. You will learn essential data engineering and feature engineering techniques, including how to split data for training and testing. Additionally, you will experiment with different algorithms and hyperparameter tuning to identify the optimal model configuration.

In this module, you’ll transition from model development to deployment. You’ll learn to package your model with FastAPI and create a user interface with Streamlit. The module focuses on containerizing the application with Docker and Docker Compose to ensure the deployment is scalable and production-ready.

This module covers the automation of MLOps pipelines using GitHub Actions for continuous integration (CI). You’ll learn to create workflows that automate the model training, testing, and deployment processes. The integration of MLflow and Docker will streamline model tracking and container management as part of the CI pipeline.

This module introduces Kubernetes as a platform for deploying scalable machine learning models in production. You will learn how to architect and deploy ML model serving infrastructure using Kubernetes, including configuring pods, services, and deployments. You'll also generate and customize Kubernetes YAML manifests to automate deployment and scaling.

In this module, you will focus on monitoring and autoscaling of machine learning models in production. Using Prometheus and Grafana, you'll implement system monitoring and visualize performance metrics. You'll also learn to automate scaling using KEDA and VPA based on resource usage, and conduct load testing to evaluate system capacity under stress.

This module introduces GitOps principles and how they can streamline deployment in MLOps. You will learn how to use ArgoCD to implement continuous delivery (CD) pipelines and manage ML/LLM application deployments. By designing end-to-end CI/CD workflows, you’ll understand how GitOps ensures a seamless, automated deployment process for machine learning models.