Training Machine Learning Models with Azure ABS Data

Azure ABS

Azure Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). It is a massively scalable, secure data lake functionality built on Azure Blob Storage which is designed for big data analytics and offers a hierarchical file system.

Machine Learning Models

Machine learning models are computer programs that are used to recognize patterns in data or make predictions. They are created from machine learning algorithms, which are trained using either labeled, unlabeled, or mixed data. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Machine learning is popular because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Meta, Google and Uber, make machine learning a central part of their operations. Machine learning is also popular because computation is abundant and cheap. Abundant and cheap computation has driven the abundance of data we are collecting and therefore the increase in capability of machine learning methods.

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