Training PyTorch Models with data in your Data Lake

Data Lake

Data lakes are popular because they provide a consolidated, centralized storage area for raw, unstructured, semi-structured, and structured data taken from multiple sources and lacking a predefined schema. They specialize in ingesting structured, semi-structured and unstructured data and provide mechanisms to easily ingest streaming data in addition to batch loads. Data lakes are open format so users avoid lock-in to a proprietary system like a data warehouse. They are also highly durable and low cost because of their ability to scale and leverage object storage.

PyTorch Models

PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It was originally developed by Meta AI and now part of the Linux Foundation umbrella. PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation. PyTorch uses dynamic computation graphs which makes it different from other deep learning frameworks. It has become a popular choice for deep learning tasks such as computer vision, natural language processing, and speech recognition. PyTorch has gained popularity for its simplicity, ease of use, dynamic computational graph, efficient memory usage, flexibility, speed, native ONNX model exports, which can be used to speed up inference. It also shares many commands with numpy which reduces the barrier to learning it.
With the growing popularity of both data lakes for storage and PyTorch models for AI deployments, it is unsurprising that many organizations are seeking to train PyTorch models using data in their data lake. Kaspian offers native connectors for the most popular data lakes. Just register your data lake as a datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy PyTorch models using any data in your cloud with minimal setup or management.
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.

Get started today

No credit card needed