Training Deep Learning Models with AWS S3 Data

AWS S3

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics. Amazon S3 is used as the primary storage platform for a data lake built on AWS because of its virtually unlimited scalability and high durability. AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches.

Deep Learning Models

Deep learning (DL) is a subset of machine learning that uses neural networks with three or more layers to simulate the behavior of the human brain. Deep learning models are popular because they can learn from large amounts of data and perform tasks that would normally require human intelligence to complete. Deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), restricted Boltzmann machines (RBMs), autoencoders, generative adversarial networks (GANs), residual neural networks (ResNets), self-organizing maps (SOMs), deep belief networks (DBNs), and multilayer perceptrons (MLPs).

With the growing popularity of both AWS S3 for storage and deep learning models for AI deployments, it is unsurprising that many organizations are seeking to train deep learning models using data in AWS S3. Kaspian offers native connectors for this operation. Just register your AWS S3 datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy deep learning 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.