Category: Amazon SageMaker Data Wrangler

Data generates new value to businesses through insights and building predictive models. However, although data is plentiful, available data scientists are far and few. Despite our attempts in recent years to produce data scientists from academia and elsewhere, we still see a huge shortage that will continue into the near future. To accelerate model building, […]

Data preparation remains a major challenge in the machine learning (ML) space. Data scientists and engineers need to write queries and code to get data from source data stores, and then write the queries to transform this data, to create features to be used in model development and training. All of this data pipeline development […]

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 […]

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 […]

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, […]