AWS Machine Learning Blog

Category: Amazon SageMaker Data Wrangler

Prepare image data with Amazon SageMaker Data Wrangler

The rapid adoption of smart phones and other mobile platforms has generated an enormous amount of image data. According to Gartner, unstructured data now represents 80–90% of all new enterprise data, but just 18% of organizations are taking advantage of this data. This is mainly due to a lack of expertise and the large amount […]

Amazon SageMaker Data Wrangler for dimensionality reduction

In the world of machine learning (ML), the quality of the dataset is of significant importance to model predictability. Although more data is usually better, large datasets with a high number of features can sometimes lead to non-optimal model performance due to the curse of dimensionality. Analysts can spend a significant amount of time transforming […]

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 […]

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and […]

Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

As organizations grow in size and scale, the complexities of running workloads increase, and the need to develop and operationalize processes and workflows becomes critical. Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice […]

Accelerate time to insight with Amazon SageMaker Data Wrangler and the power of Apache Hive

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources. […]

Achieve rapid time-to-value business outcomes with faster ML model training using Amazon SageMaker Canvas

Machine learning (ML) can help companies make better business decisions through advanced analytics. Companies across industries apply ML to use cases such as predicting customer churn, demand forecasting, credit scoring, predicting late shipments, and improving manufacturing quality. In this blog post, we’ll look at how Amazon SageMaker Canvas delivers faster and more accurate model training times enabling […]

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors. For example, in the healthcare industry, ML-driven analytics can be used for diagnostic assistance and […]

Introducing Amazon SageMaker Data Wrangler’s new embedded visualizations

Manually inspecting data quality and cleaning data is a painful and time-consuming process that can take a huge chunk of a data scientist’s time on a project. According to a 2020 survey of data scientists conducted by Anaconda, data scientists spend approximately 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), […]

Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler

Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of their time on data preparation. In this intensive competitive market, teams want to analyze data and extract more meaningful insights quickly. Customers are adopting more efficient and visual ways to build […]