AWS Machine Learning Blog

Category: Artificial Intelligence

Enhance Amazon Lex with conversational FAQ features using LLMs

Amazon Lex is a service that allows you to quickly and easily build conversational bots (“chatbots”), virtual agents, and interactive voice response (IVR) systems for applications such as Amazon Connect. Artificial intelligence (AI) and machine learning (ML) have been a focus for Amazon for over 20 years, and many of the capabilities that customers use […]

Enhance Amazon Lex with LLMs and improve the FAQ experience using URL ingestion

In today’s digital world, most consumers would rather find answers to their customer service questions on their own rather than taking the time to reach out to businesses and/or service providers. This blog post explores an innovative solution to build a question and answer chatbot in Amazon Lex that uses existing FAQs from your website. […]

Build an email spam detector using Amazon SageMaker

Spam emails, also known as junk mail, are sent to a large number of users at once and often contain scams, phishing content, or cryptic messages. Spam emails are sometimes sent manually by a human, but most often they are sent using a bot. Examples of spam emails include fake ads, chain emails, and impersonation […]

Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart

October 2023: This post was reviewed and updated with support for finetuning. Today, we are excited to announce that Llama 2 foundation models developed by Meta are available for customers through Amazon SageMaker JumpStart to fine-tune and deploy. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative […]

Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface. You can perform all ML development […]

Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

Recent years have shown amazing growth in deep learning neural networks (DNNs). This growth can be seen in more accurate models and even opening new possibilities with generative AI: large language models (LLMs) that synthesize natural language, text-to-image generators, and more. These increased capabilities of DNNs come with the cost of having massive models that […]

Access private repos using the @remote decorator for Amazon SageMaker training workloads

As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. Many organizations prefer writing their ML code in a production-ready style in the form of Python methods and classes as opposed to an exploratory style […]

Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

Amazon SageMaker is an end-to-end machine learning (ML) platform with wide-ranging features to ingest, transform, and measure bias in data, and train, deploy, and manage models in production with best-in-class compute and services such as Amazon SageMaker Data Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Model Registry, Amazon SageMaker Feature Store, Amazon SageMaker […]

Highlight text as it’s being spoken using Amazon Polly

Amazon Polly is a service that turns text into lifelike speech. It enables the development of a whole class of applications that can convert text into speech in multiple languages. This service can be used by chatbots, audio books, and other text-to-speech applications in conjunction with other AWS AI or machine learning (ML) services. For […]

Solution overview

Predict vehicle fleet failure probability using Amazon SageMaker Jumpstart

Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle failures and scheduling maintenance and repairs, you’ll reduce downtime, improve safety, and boost productivity levels. What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned […]