AWS Machine Learning Blog
Category: Amazon SageMaker Data Wrangler
Prepare data for predicting credit risk using Amazon SageMaker Data Wrangler and Amazon SageMaker Clarify
For data scientists and machine learning (ML) developers, data preparation is one of the most challenging and time-consuming tasks of building ML solutions. In an often iterative and highly manual process, data must be sourced, analyzed, cleaned, and enriched before it can be used to train an ML model. Typical tasks associated with data preparation […]
Enable cross-account access for Amazon SageMaker Data Wrangler using AWS Lake Formation
Amazon SageMaker Data Wrangler is the fastest and easiest way for data scientists to prepare data for machine learning (ML) applications. With Data Wrangler, you can simplify the process of feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization through a single visual interface. Data Wrangler […]
Exploratory data analysis, feature engineering, and operationalizing your data flow into your ML pipeline with Amazon SageMaker Data Wrangler
According to The State of Data Science 2020 survey, data management, exploratory data analysis (EDA), feature selection, and feature engineering accounts for more than 66% of a data scientist’s time (see the following diagram). The same survey highlights that the top three biggest roadblocks to deploying a model in production are managing dependencies and environments, […]