Category: Amazon Machine Learning

This post is co-written with Aurélien Capdecomme and Bertrand d’Aure from 20 Minutes. With 19 million monthly readers, 20 Minutes is a major player in the French media landscape. The media organization delivers useful, relevant, and accessible information to an audience that consists primarily of young and active urban readers. Every month, nearly 8.3 million 25–49-year-olds choose […]

While Generative AI can create highly realistic content, including text, images, and videos, it can also generate outputs that appear plausible but are verifiably incorrect. Incorporating human judgment is crucial, especially in complex and high-risk decision-making scenarios. This involves building a human-in-the-loop process where humans play an active role in decision making alongside the AI system. In this blog post, you will learn about prompt chaining, how to break a complex task into multiple tasks to use prompt chaining with an LLM in a specific order, and how to involve a human to review the response generated by the LLM.

Crafting new questions for exams and quizzes can be tedious and time-consuming for educators. The time required varies based on factors like subject matter, question types, experience level, and class level. Multiple-choice questions require substantial time to generate quality distractors and ensure a single unambiguous answer, and composing effective true-false questions demands careful effort to […]

In this post, we provide an overview of the state-of-the-art embedding models by Voyage AI and show a RAG implementation with Voyage AI’s text embedding model on Amazon SageMaker Jumpstart, Anthropic’s Claude 3 model on Amazon Bedrock, and Amazon OpenSearch Service. Voyage AI’s embedding models are the preferred embedding models for Anthropic. In addition to general-purpose embedding models, Voyage AI offers domain-specific embedding models that are tuned to a particular domain.

Amazon Titan Text Premier, the latest addition to the Amazon Titan family of large language models (LLMs), is now generally available in Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and […]

In this post, we provide a comparison of results obtained by two such techniques: zero-shot and few-shot prompting. We also explore the utility of the RAG prompt engineering technique as it applies to the task of summarization.
Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline.

In today’s technological landscape, artificial intelligence (AI) and machine learning (ML) are becoming increasingly accessible, enabling builders of all skill levels to harness their power. As more companies adopt AI solutions, there’s a growing need to upskill both technical and non-technical teams in responsibly expanding AI usage. Getting hands-on experience is crucial for understanding and […]

This post is co-written with Tom Famularo, Abhay Shah and Nicolette Kontor from Verisk. Verisk (Nasdaq: VRSK) is a leading data analytics and technology partner for the global insurance industry. Through advanced analytics, software, research, and industry expertise across over 20 countries, Verisk helps build resilience for individuals, communities, and businesses. The company is committed […]

The telecommunications industry is more competitive than ever before. With customers able to easily switch between providers, reducing customer churn is a crucial priority for telecom companies who want to stay ahead. To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This post explores the […]

Unlock ML governance with SageMaker-DataZone integration: streamline infrastructure, collaborate, and govern data/ML assets.