Training Neural Network 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.

Neural Network Models

Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. They rely on training data to learn and improve their accuracy over time. Neural networks simulate how the brain learns by using multiple layers of nodes (input, hidden, and output) and they're able to learn both in supervised and unsupervised situations. They can recognize hidden patterns and correlations in raw data, cluster and classify it, and, over time, continuously learn and improve. Neural networks have many applications such as image recognition, speech recognition, natural language processing, autonomous vehicles, robotics, and more.

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