Training Computer Vision 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.

Computer Vision Models

Computer vision (CV) is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs and take actions or make recommendations based on that information. It trains convolutional neural networks to develop human-like vision capabilities for applications. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they see. Computer vision has many applications such as facial recognition, autonomous vehicles, medical imaging, robotics, surveillance, and more.

Open source orchestrators like Airflow are one of the primary means by which companies train computer vision 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.