AWS Machine Learning Blog

Category: Intermediate (200)

Index your Confluence content using the new Confluence connector V2 for Amazon Kendra

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should […]

Announcing the updated Microsoft SharePoint connector (V2.0) for Amazon Kendra

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. Amazon Kendra can pull together […]

Prepare training and validation dataset for facies classification using a Snowflake OAuth connection and Amazon SageMaker Canvas

February 2024: This post was reviewed and updated for accuracy. This post is co-written with Thatcher Thornberry from bpx energy.  Facies classification is the process of segmenting lithologic formations from geologic data at the wellbore location. During drilling, wireline logs are obtained, which have depth-dependent geologic information. Geologists are deployed to analyze this log data […]

Introducing Amazon Textract Bulk Document Uploader for enhanced evaluation and analysis

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. To make it simpler to evaluate the capabilities of Amazon Textract, we have launched a new Bulk Document Uploader feature on the Amazon Textract console that enables you to quickly process your own set of […]

AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio—a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial up or down the […]

Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry, enabling you to operationalize ML models in production. Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own—without requiring any ML experience or having to […]

Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs. Examples of such use cases include scaling up […]

Cyclical forecasting workflow

Automate the deployment of an Amazon Forecast time-series forecasting model

Time series forecasting refers to the process of predicting future values of time series data (data that is collected at regular intervals over time). Simple methods for time series forecasting use historical values of the same variable whose future values need to be predicted, whereas more complex, machine learning (ML)-based methods use additional information, such […]

Get started with generative AI on AWS using Amazon SageMaker JumpStart

Generative AI is gaining a lot of public attention at present, with talk around products such as GPT4, ChatGPT, DALL-E2, Bard, and many other AI technologies. Many customers have been asking for more information on AWS’s generative AI solutions. The aim of this post is to address those needs. This post provides an overview of […]

How Vericast optimized feature engineering using Amazon SageMaker Processing

This post is co-written by Jyoti Sharma and Sharmo Sarkar from Vericast. For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. […]