Streamlit Exercise - In this lab, we implement a Simple Linear Regression model that follows the pattern Y = 2X + 3 + noise and create a user-friendly Streamlit interface to interact with the model. The dashboard allows users to input X values and get Y predictions from the trained linear regression model.
Airflow Exercise - This lab demonstrates orchestrating machine learning workflows using Apache Airflow. The project implements a complete ML pipeline using directed acyclic graphs (DAGs) to automate data generation, preprocessing, model training, and evaluation tasks within a Dockerized Airflow environment.
GitHub Exercise - This lab focuses on 5 modules, which includes creating a virtual environment, creating a GitHub repository, creating Python files, creating test files using pytest and unittest, and implementing GitHub Actions
MLFlow Exercise - Logging experiments (parameters and metrics) and Serving Best Recall model
Docker Lab - host a webapp on a Docker container (model to classify handwritten digits)
Terraform Lab - Code - deploy the infra required to host LAB5's model. Upgrade the config (new memory setting) and reapply the changes.