Category: Technical How-to

Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered across multiple data souces in an enterprise, including commonly used document […]

Reliability managers and technicians in industrial environments such as manufacturing production lines, warehouses, and industrial plants are keen to improve equipment health and uptime to maximize product output and quality. Machine and process failures are often addressed by reactive activity after incidents happen or by costly preventive maintenance, where you run the risk of over-maintaining […]

“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 […]

As more businesses increase their online presence to serve their customers better, new fraud patterns are constantly emerging. In today’s ever-evolving digital landscape, where fraudsters are becoming more sophisticated in their tactics, detecting and preventing such fraudulent activities has become paramount for companies and financial institutions. Traditional rule-based fraud detection systems are capped in their […]

RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. […]

Globally, many organizations have critical business data dispersed among various content repositories, making it difficult to access this information in a streamlined and cohesive manner. Creating a unified and secure search experience is a significant challenge for organizations because each repository contains a wide range of document formats and access control mechanisms. Amazon Kendra is […]

Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit, developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As a data scientist, you may want to […]

Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. These enterprise customers that are starting to adopt AWS, expanding their footprint on […]

Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that […]

This is a joint post by NXP SEMICONDUCTORS N.V. & AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. In this post, we demonstrate how NXP, an industry leader in the semiconductor sector, […]