Set up an Airflow Environment on AWS in Minutes

Set up an Airflow Environment on AWS in Minutes

Set up an Airflow Environment on AWS in Minutes. Steps: AWS account setup. Create a test DAG and upload it to S3. Write a requirements. txt file to include open source packages in your environment. Create an Airflow environment in the AWS console. Access the Airflow UI.

Getting Started with Amazon Managed Apache Airflow

Apache Airflow is a powerful platform for scheduling and monitoring data pipelines, machine learning workflows, and DevOps deployments. In this post, we’ll cover how to set up an Airflow environment on AWS and start scheduling workflows in the cloud.

This is part one of a series, Scheduling Machine Learning Workflows. Airflow allows you to schedule your data pulls and featuring engineering pipelines, model training, model deployment, and much more.The content in this tutorial is applicable to anyone who wants to learn how to create, schedule, and monitor their production workflows in an Amazon Airflow environment — not just machine learning engineers.

  1. Set up an Airflow Environment on AWS (this post)
  2. More coming soon…

Motivation

Whether you’re an aspiring to a career in engineering or data science, the skill-set of programmatically processing data and building systems to consume that data downstream (e.g. machine learning models) will likely be vital to your success. There’s a reason why these skills are in hot demand.

aws airflow machine-learning

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