AWS Machine Learning Blog

Category: Learning Levels

Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

Generative AI models have been experiencing rapid growth in recent months due to its impressive capabilities in creating realistic text, images, code, and audio. Among these models, Stable Diffusion models stand out for their unique strength in creating high-quality images based on text prompts. Stable Diffusion can generate a wide variety of high-quality images, including […]

Use generative AI foundation models in VPC mode with no internet connectivity using Amazon SageMaker JumpStart

With recent advancements in generative AI, there are lot of discussions happening on how to use generative AI across different industries to solve specific business problems. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It is all backed by very large models […]

Analyze rodent infestation using Amazon SageMaker geospatial capabilities

Rodents such as rats and mice are associated with a number of health risks and are known to spread more than 35 diseases. Identifying regions of high rodent activity can help local authorities and pest control organizations plan for interventions effectively and exterminate the rodents. In this post, we show how to monitor and visualize […]

Enel automates large-scale power grid asset management and anomaly detection using Amazon SageMaker

This is a guest post by Mario Namtao Shianti Larcher, Head of Computer Vision at Enel. Enel, which started as Italy’s national entity for electricity, is today a multinational company present in 32 countries and the first private network operator in the world with 74 million users. It is also recognized as the first renewables […]

Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker

Artificial intelligence (AI) has become an important and popular topic in the technology community. As AI has evolved, we have seen different types of machine learning (ML) models emerge. One approach, known as ensemble modeling, has been rapidly gaining traction among data scientists and practitioners. In this post, we discuss what ensemble models are and […]

Use a generative AI foundation model for summarization and question answering using your own data

Large language models (LLMs) can be used to analyze complex documents and provide summaries and answers to questions. The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset. Once you have a solid LLM, you’ll want to expose that LLM to […]

Integrate Amazon SageMaker Model Cards with the model registry

Amazon SageMaker Model Cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation […]

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 […]