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In this example, we will demonstrate using current data within a Netezza Performance Server as a Service (NPSaaS) table combined with historical data in Parquet files to determine if flight delays have increased in 2022 due to the impact of the COVID-19 pandemic on the airline travel industry. This demonstration illustrates how Netezza Performance Server …

Read more “How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)”

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so …

Read more “Best practices for load testing Amazon SageMaker real-time inference endpoints”

Introduction Most machine learning models require the input features to be in numerical format and if the features are in categorial format, pre-processing steps such as one-hot encoding are needed to convert them into numerical format. Converting a large number of categorical values may lead to creating sparse features, a set of features that contains …

Read more “Sparse Features Support in BigQuery”