Artificial Intelligence
Author: Meenakshisundaram Thandavarayan
Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy
“Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and systematic. In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving […]
Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler
Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of […]
Import data from cross-account Amazon Redshift in Amazon SageMaker Data Wrangler for exploratory data analysis and data preparation
Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations with a multi-account architecture want to avoid situations where they must extract data from one […]