Category: Amazon SageMaker Data Wrangler

Companies use time series forecasting to make core planning decisions that help them navigate through uncertain futures. This post is meant to address supply chain stakeholders, who share a common need of determining how many finished goods are needed over a mixed variety of planning time horizons. In addition to planning how many units of […]

In this post, we walk you through importing data from, and exporting data to, an S3 access point in SageMaker Data Wrangler.

We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

Data preparation is a critical step in any data-driven project, and having the right tools can greatly enhance operational efficiency. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of […]

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are […]

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code. SageMaker Data Wrangler supports Snowflake, a popular […]

Every organization has its own set of standards and practices that provide security and governance for their AWS environment. Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. SageMaker provides a set of templates […]

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 […]

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 […]

“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 […]