Training Natural Language Processing Models with Snowflake Data

Snowflake

Snowflake is a cloud-based data warehousing company that provides a platform for data storage, processing and analysis. It is considered cloud-agnostic, as it operates across Amazon Web Services (AWS), Microsoft Azure or Google Cloud. Snowflake delivers a platform that is fast, flexible, and user-friendly. It provides the means for not only data storage but also processing and analysis. One of the main reasons that Snowflake is gaining recognition as the top cloud data warehousing solution is thanks to its architecture, which consists of dynamic, scalable computing power with usage-based charges out-of-the-box features such as data cloning and sharing, on-the-fly scalable computing and third-party tool support.

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 Snowflake 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 Snowflake. Kaspian offers native connectors for this operation. Just register your Snowflake 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.