Training Machine Learning Models using Airflow

Airflow

Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. It was first developed by Airbnb and is now under the Apache Software Foundation. Airflow uses Python to create workflows that can be easily scheduled and monitored. Airflow can help you move data from one source to a destination, filter datasets, apply data policies, manipulation, monitoring and even call microservices to trigger database management tasks. It can be used for batch jobs, organizing, monitoring, and executing workflows automatically. Airflow has been used by many companies for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more.

Machine Learning Models

Machine learning models are computer programs that are used to recognize patterns in data or make predictions. They are created from machine learning algorithms, which are trained using either labeled, unlabeled, or mixed data. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Machine learning is popular because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Meta, Google and Uber, make machine learning a central part of their operations. Machine learning is also popular because computation is abundant and cheap. Abundant and cheap computation has driven the abundance of data we are collecting and therefore the increase in capability of machine learning methods.

Open source orchestrators like Airflow are one of the primary means by which companies train machine learning models in production. Airflow offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Airflow; this operator makes it easy to either swap to or get started with training pipelines that utilize Kaspian's flexible compute layer, with native support for autoscaling, GPU acceleration, and more.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.