Innovations in artificial intelligence (AI) and machine learning (ML) are causing organizations to take a fresh look at the possibilities these technologies can offer. As you aim to bring your proofs of concept to production at an enterprise scale, you may experience challenges aligning with the strict security compliance requirements of their organization. In the …
With last month’s blog, I started a series of posts that highlight the key factors that are driving customers to choose Amazon Bedrock. I explored how Bedrock enables customers to build a secure, compliant foundation for generative AI applications. Now I’d like to turn to a slightly more technical, but equally important differentiator for Bedrock—the …
Build custom generative AI applications powered by Amazon Bedrock Read More »
Generative artificial intelligence (AI) models have opened up new possibilities for automating and enhancing software development workflows. Specifically, the emergent capability for generative models to produce code based on natural language prompts has opened many doors to how developers and DevOps professionals approach their work and improve their efficiency. In this post, we provide an …
Music generation models have emerged as powerful tools that transform natural language text into musical compositions. Originating from advancements in artificial intelligence (AI) and deep learning, these models are designed to understand and translate descriptive text into coherent, aesthetically pleasing music. Their ability to democratize music production allows individuals without formal training to create high-quality …
Inference AudioCraft MusicGen models using Amazon SageMaker Read More »
Google for Startups AI Academy: American Infrastructure provides mentorship, training, and resources to help founders scale solutions and benefit communities.
Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and foundation models (FMs). RAG models first retrieve relevant information from a large corpus of text and then use a FM to synthesize an answer based on the retrieved information. An end-to-end RAG solution involves several …
In recent years, we have seen a big increase in the size of large language models (LLMs) used to solve natural language processing (NLP) tasks such as question answering and text summarization. Larger models with more parameters, which are in the order of hundreds of billions at the time of writing, tend to produce better …
Faster LLMs with speculative decoding and AWS Inferentia2 Read More »
Information retrieval systems have powered the information age through their ability to crawl and sift through massive amounts of data and quickly return accurate and relevant results. These systems, such as search engines and databases, typically work by indexing on keywords and fields contained in data files. However, much of our data in the digital …
This is a guest post co-written with Vicente Cruz Mínguez, Head of Data and Advanced Analytics at Cepsa Química, and Marcos Fernández Díaz, Senior Data Scientist at Keepler. Generative artificial intelligence (AI) is rapidly emerging as a transformative force, poised to disrupt and reshape businesses of all sizes and across industries. Generative AI empowers organizations …
GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions of entities, which are inherently embedded in most real-world …
GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs Read More »
