Automate ML Pipelines Using Apache Airflow
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Explore how to automate machine learning pipelines using Apache Airflow. Learn how to schedule, monitor, and manage end-to-end workflows for ML tasks, streamlining model training, evaluation, and deployment in production environments.
At a Glance
By mastering Apache Airflow you will gain hands-on experience in building a KNN classification model for the Iris dataset, using Apache Airflow for workflow automation. You will also have learned how to deploy the trained model for prediction, and how to generate a DAG ( Directed Acyclic Graph ) for a data pipeline. It will increase productivity, reduce costs, and have faster time-to-insight. These skills are essential for any data scientist or engineer working on classification tasks and data pipelines and can be applied to a wide range of other datasets and workflows.
Why you should do this Guided Project
The Iris dataset is a well-known and widely-used dataset in the field of machine learning. It consists of measurements of three species of iris flowers and is commonly used as a benchmark dataset for classification models. In this project, you will gain hands-on experience in building a classification model using the K-Nearest Neighbors ( KNN ) algorithm, which is a popular machine-learning algorithm for classification tasks.
This project provides a structured approach to building a classification model that can be easily adapted to other datasets and workflows. The use of Apache Airflow allows for the automation of the entire process, from data preprocessing to model evaluation and deployment, making it easy to incorporate this workflow into your projects.
It is an opportunity to learn and practice using Apache Airflow, an open-source platform for programmatically creating, scheduling, and monitoring workflows. Airflow provides a user-friendly interface for building, testing, and deploying data pipelines, making it an essential tool for any data scientist or engineer.
A Look at the Project Ahead
- Understand the K-Nearest Neighbors (KNN) algorithm and its use in classification tasks.
- Implement an Apache Airflow workflow to automate the process of data preprocessing, model training, and evaluation.
- Using Airflow to schedule and monitor the execution of the workflow, and to visualise the results.
- Learn how to create a DAG ( Directed Acyclic Graph ) using Apache Airflow, which is a collection of tasks and dependencies that represent a data pipeline.
What You’ll Need
This course mainly uses Python. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
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