![]() The metadata database stores all metadata used by Airflow such as user profiles, and information of the DAGs (Directed Acyclic Graphs). The scheduler which is a daemon built using the Python Daemon library and is responsible for scheduling the data pipelines. The web server which is a flask server running with Gunicorn is in charge of serving the UI dashboard. A key difference between Airflow and the other orchestrators is the fact that data pipelines are defined as code and tasks are instantiated dynamically. With Airflow, we can manage workflows as scripts, monitor them via the user interface (UI), and extend their functionality through a set of powerful plugins.īefore we start explaining the system, we need to briefly explain what Apache Airflow is and its pros and cons.Īpache Airflow is a way to programmatically author, schedule, and monitor data pipelines. A Jupyter Notebook in Sagemaker to understand the individual ML tasks in detail, such as data exploration, data preparation, and model training/tuning for a classification problem.Īs the volume and complexity of our data processing pipelines increase, we can employ Airflow to simplify the overall process by decomposing it into a series of smaller tasks and coordinate the execution of these tasks as part of a workflow.Airflow DAG Python Script that integrates and orchestrates all the ML tasks in a ML workflow.A CloudFormation stack which integrates Airflow with AWS.A CloudFormation stack which contains the required resources, e.g., S3 bucket, EC2 instance, etc.Once the training job is finished, it automatically shuts down the EC2 machine to reduce the costs. For this problem, I propose designing a CloudFormation stack and using Amazon Sagemaker and Apache Airflow. One of the challenges that we faced in one of the clients’ projects was finding the best optimal solution to run the training job to automatically shut down the engine of EC2 machine, which was a GPU P3.2xlarge. This blog aims to explain an overview of Apache Airflow integration with AWS and design an architecture to build, manage and orchestrate machine learning workflows using Amazon Sagemaker. Design and implement a complete Machine Learning workflow with Amazon Sagemaker
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