Artificial Intelligence
Author: Paul Hargis
Paul Hargis has focused his efforts on machine learning at several companies, including AWS, Amazon, and Hortonworks. He enjoys building technology solutions and teaching people how to leverage them. Paul likes to help customers expand their machine learning initiatives to solve real-world problems. Prior to his role at AWS, he was lead architect for Amazon Exports and Expansions, helping amazon.com improve the experience for international shoppers.
Speed up delivery of ML workloads using Code Editor in Amazon SageMaker Unified Studio
In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.
Build machine learning-ready datasets from the Amazon SageMaker offline Feature Store using the Amazon SageMaker Python SDK
Amazon SageMaker Feature Store is a purpose-built service to store and retrieve feature data for use by machine learning (ML) models. Feature Store provides an online store capable of low-latency, high-throughput reads and writes, and an offline store that provides bulk access to all historical record data. Feature Store handles the synchronization of data between […]
Reduce cost and development time with Amazon SageMaker Pipelines local mode
Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we […]
Load and transform data from Delta Lake using Amazon SageMaker Studio and Apache Spark
Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may include both structured and unstructured data formats. Having a centralized data store for all types of data […]
Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark
This post is co-written with Raphey Holmes, Software Engineering Manager, and Jason Mackay, Principal Software Development Engineer, at GoDaddy. GoDaddy is the world’s largest services platform for entrepreneurs around the globe, empowering their worldwide community of over 20 million customers—and entrepreneurs everywhere—by giving them all the help and tools they need to grow online. GoDaddy […]
Using streaming ingestion with Amazon SageMaker Feature Store to make ML-backed decisions in near-real time
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Businesses are increasingly using machine learning (ML) to make near-real time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing […]