Category: Amazon Machine Learning

In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face. In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried.

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. This technique is achieved through the use of ML algorithms that enable the understanding of the meaning and context of data (semantic […]

In this post, we show you how to securely create a movie chatbot by implementing RAG with your own data using Knowledge Bases for Amazon Bedrock. We use the IMDb and Box Office Mojo dataset to simulate a catalog for media and entertainment customers and showcase how you can build your own RAG solution in just a couple of steps.

This post was co-written with Ricardo Perdigao, Solution Architecture Manager at Mendix, a Siemens business. Mendix, a Siemens business, offers the low-code platform with the vision and execution designed for today’s complex software development challenges. Since 2005, we’ve helped thousands of organizations worldwide reimagine how they develop applications with our platform’s cutting-edge capabilities. Mendix allows […]

With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform a range of generative tasks such as question answering, summarization, and content creation on text data. However, real-world data exists in multiple modalities, such as text, images, video, and audio. Take […]

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the time it takes to generate a single token, and throughput, defined by the number of tokens generated per second. Although a single request to the deployed endpoint would exhibit a throughput […]

This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs. We first delve into the vulnerabilities, threats, and risks that arise from the implementation, deployment, and use of LLM solutions, and provide guidance on how to start innovating with security in mind. We then discuss how building on a secure foundation is essential for generative AI. Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries.

This post discusses how enterprises can build accurate, transparent, and secure generative AI applications while keeping full control over proprietary data. The proposed solution is a RAG pipeline using an AI-native technology stack, whose components are designed from the ground up with AI at their core, rather than having AI capabilities added as an afterthought. We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace.

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained language models (PLMs) are undergoing rapid commercial and enterprise adoption in the areas of productivity tools, customer service, search and recommendations, business process automation, and […]

Enterprises have access to massive amounts of data, much of which is difficult to discover because the data is unstructured. Conventional approaches to analyzing unstructured data use keyword or synonym matching. They don’t capture the full context of a document, making them less effective in dealing with unstructured data. In contrast, text embeddings use machine […]