Favorite This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct

Read More
Shared by AWS Machine Learning September 19, 2023

Favorite Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data

Read More
Shared by AWS Machine Learning September 19, 2023

Favorite This post is co-authored with Richard Alexander and Mark Hallows from Arup. Arup is a global collective of designers, consultants, and experts dedicated to sustainable development. Data underpins Arup consultancy for clients with world-class collection and analysis providing insight to make an impact. The solution presented here is to

Read More
Shared by AWS Machine Learning September 18, 2023

Favorite Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated

Read More
Shared by AWS Machine Learning September 18, 2023

Favorite Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases,

Read More
Shared by AWS Machine Learning September 15, 2023

Favorite This post takes you through the most common challenges that customers face when searching internal documents, and gives you concrete guidance on how AWS services can be used to create a generative AI conversational bot that makes internal information more useful. Unstructured data accounts for 80% of all the

Read More
Shared by AWS Machine Learning September 14, 2023

Favorite Today, generative AI models cover a variety of tasks from text summarization, Q&A, and image and video generation. To improve the quality of output, approaches like n-short learning, Prompt engineering, Retrieval Augmented Generation (RAG) and fine tuning are used. Fine-tuning allows you to adjust these generative AI models to

Read More
Shared by AWS Machine Learning September 14, 2023

Favorite “Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data,

Read More
Shared by AWS Machine Learning September 14, 2023

Favorite Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can

Read More
Shared by AWS Machine Learning September 13, 2023

Favorite This is a guest post from Scalable Capital, a leading FinTech in Europe that offers digital wealth management and a brokerage platform with a trading flat rate. As a fast-growing company, Scalable Capital’s goals are to not only build an innovative, robust, and reliable infrastructure, but to also provide

Read More
Shared by AWS Machine Learning September 12, 2023