Welcome to the MLflow group assignment! Use MLflow within one of your previous projects and save structured and unstructured information related to your trained model in SQLite within MLflow. Optionally, prepare a ML app based on three layers as Data, Business, Presentation Layers.
For this assignment, you will be using one of your previous projects to train a machine learning model and track and manage your machine learning experiments using MLflow. The goal of this assignment is to help you gain hands-on experience in using MLflow to manage and track your machine learning experiments, and optionally in preparing an ML app based on three layers to provide a user-friendly interface for interacting with your machine learning model. Good luck and have fun!
Select one of your previous projects that includes a machine learning component and use MLflow to track and manage your machine learning experiments. The following tasks should be performed:
Train a machine learning model using the data from your previous project. You can use any machine learning model that is appropriate for your data and problem.
Use MLflow to track and manage your machine learning experiments. Log the hyperparameters, metrics, and artifacts of your machine learning experiments in MLflow. Save structured and unstructured information related to your trained model in SQLite within MLflow.
Optionally, prepare an ML app based on three layers (data, business, presentation) to provide a user-friendly interface for interacting with your machine learning model. This will involve creating a data layer that handles the data processing pipeline and provides functions for loading and preprocessing the data, a business layer that implements the machine learning model and its related functions, and a presentation layer that implements the user interface and connects it to the business layer.
Select one of your previous projects that includes a machine learning component.