Training Natural Language Processing Models with Redshift Data

Redshift

Amazon Redshift is a popular data warehousing solution that can handle data on an exabytes scale. It is useful for processing real-time analytics, combining multiple data sources, log analysis, or more. Redshift uses parallel-processing and compression to decrease command execution time. This allows Redshift to perform operations on billions of rows at once. This also makes Redshift useful for storing and analyzing large quantities of data from logs or live feeds through a source such as Amazon Kinesis Data Firehose.

Natural Language Processing Models

Natural language processing (NLP) refers to the branch of computer science, and more specifically, the branch of artificial intelligence or AI-concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics-rule-based modeling of human language with statistical machine learning algorithms. NLP has been used in many applications such as chatbots, sentiment analysis, speech recognition, machine translation, and more.

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