Training Tensorflow Models using Prefect

Prefect

Prefect is an open-source workflow management system that allows you to build, schedule, and monitor data workflows. It enables you to transform any Python function into a unit of work that can be observed and orchestrated. Prefect can be used for various use cases such as ETL pipelines, machine learning workflows, data warehousing, and more. It has a dynamic engine and ephemeral API that makes it easy to run workflows interactively during the building phase. Prefect also offers the ability to cache and persist inputs and outputs for large files and expensive operations, improving development time when debugging.

Tensorflow Models

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow helped launch the deep learning revolution: with pre-trained models, data, and high-level APIs, it has become easy for everyone to build ML and DL models. One of the important Tensorflow features is that it is flexible in its operating model, meaning it has modularity and the parts of it that you want to make standalone it offers you that option. TensorFlow.js is a JavaScript-based framework to run machine learning models within the browser. Any modern browser can run the TensorFlow model with no changes to the code.

Open source orchestrators like Prefect are one of the primary means by which companies train Tensorflow models in production. Prefect offers a mechanism to schedule and monitor these jobs as part of more complex workflow graphs. Kaspian has a native operator for Prefect; 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.