“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 data preparation and feature engineering. This can be a tedious task involving data collection, discovery, profiling, cleansing, structuring, transforming, enriching, validating, and securely storing the data.
Amazon SageMaker Data Wrangler is a service in Amazon SageMaker Studio that provides an end-to-end solution to import, prepare, transform, featurize, and analyze data using little to no coding. You can integrate a Data Wrangler data preparation flow into your machine learning (ML) workflows to simplify data preprocessing and feature engineering, taking data preparation to production faster without the need to author PySpark code, install Apache Spark, or spin up clusters.
For

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