AWS Machine Learning Blog

Category: Artificial Intelligence

Use a data-centric approach to minimize the amount of data required to train Amazon SageMaker models

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements. Applying these techniques allows ML practitioners […]

Use Snowflake as a data source to train ML models with Amazon SageMaker

May 2023: This blog post has been updated to include a workflow that does not require building a custom container. Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. […]

How Marubeni is optimizing market decisions using AWS machine learning and analytics

This post is co-authored with Hernan Figueroa, Sr. Manager Data Science at Marubeni Power International. Marubeni Power International Inc (MPII) owns and invests in power business platforms in the Americas. An important vertical for MPII is asset management for renewable energy and energy storage assets, which are critical to reduce the carbon intensity of our […]

Portfolio optimization through multidimensional action optimization using Amazon SageMaker RL

Reinforcement learning (RL) encompasses a class of machine learning (ML) techniques that can be used to solve sequential decision-making problems. RL techniques have found widespread applications in numerous domains, including financial services, autonomous navigation, industrial control, and e-commerce. The objective of an RL problem is to train an agent that, given an observation from its […]

Hosting YOLOv8 PyTorch models on Amazon SageMaker Endpoints

Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. However, Amazon SageMaker endpoints provide a simple solution for deploying and scaling your machine learning (ML) model inferences. Our last blog post and GitHub repo on hosting a YOLOv5 TensorFlowModel on Amazon SageMaker Endpoints sparked a lot of interest […]

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, […]

solution architecture

AI/ML-driven actionable insights and themes for Amazon third-party sellers using AWS

The Amazon International Seller Growth (ISG) team runs the CSBA (Customer Service by Amazon) program that supports over 200,000 third-party Merchant Fulfilled Network (MFN) sellers. Amazon call centers facilitate hundreds of thousands of phone calls, chats, and emails going between the consumers and Amazon MFN sellers. The large volume of contacts creates a challenge for […]

Announcing the Yammer connector for Amazon Kendra

Yammer is a social networking platform designed for open and dynamic communications and collaborations within organizations. It allows you to build communities of interest, gather ideas and feedback, and keep everyone informed. It’s available via browser or mobile app, and provides a variety of common social networking features such as private and public communities, news […]

Training large language models on Amazon SageMaker: Best practices

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions (BERT) to over a trillion parameters (MiCS), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and […]

Index your Microsoft Exchange content using the Exchange connector 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 […]