Category: Amazon Machine Learning

In a recent webinar, Slalom and AWS showcased the incredible potential of chat-based enterprise search powered by AWS generative AI services like Amazon Bedrock. We’re excited to share key takeaways and a more in-depth exploration of the transformative landscape that chat-based search creates. Learn how technologies like Amazon Bedrock empower businesses to build intelligent chat-based interfaces that allow employees to interact with company data conversationally.

Neo4j is a graph data platform that, together with Amazon Bedrock, offers a compelling value add to any enterprise. Learn about the knowledge retrieval and extraction processes and review a couple of retrieval augmented generation (RAG) application architectures. Using Amazon Bedrock and Neo4j, you can simplify the knowledge extraction process which can be more complex and manual using traditional NLP libraries.

The ability to accurately share and analyze patient information between different healthcare providers and systems is critical to the transition to patient-centric care. Learn how AWS and Accenture collaborated to build a population-scale research cohort analytics solution called Accenture Health Analytics (AHA) which contains 54 million longitudinal patient records using a range of AWS services. It helps healthcare organizations improve patient outcomes and reduce delivery costs.

A common challenge faced by many companies involves the requirement to enhance the clarity and availability of internal documents. These scenarios present significant hurdles for support teams, business users, and new members who often encounter difficulties locating the relevant documentation. This post discusses how Infosys built an enterprise knowledge management assistant using generative AI technologies on AWS.

The transformative power of artificial intelligence (AI) is something Amazon and Deloitte know well. With over 40 years combined experience in the AI space, these leading organizations are innovating and using AI technologies to change how businesses work, grow, and thrive. The journey continues with Amazon Bedrock, the easiest way to build and scale with foundation models (FMs), and in this post we’ll share how Deloitte can help customers benefit from AWS’s generative AI offerings.

Commercial aircraft maintenance, repair, and overhaul (MRO) is a global business requiring unique scheduled and unscheduled maintenance, as well as regulatory compliance to ensure airworthiness for continued operation. Learn how an Infosys generative AI-based solution built on Amazon Bedrock enhanced productivity and leveled the playing field for newcomers in MRO. The solution aims to reduce the expenditure on document search, analysis, interpretation, and management.

Migrating to a new hosting provider to save costs presents an opportunity to fine-tune application performance and the DevOps processes supporting a company’s applications. This was the case for MasterWorks, which is based near Seattle and helps move the hearts and minds of people to act for Christian ministries across America. Learn how Avahi Technologies and AWS collaborated to help MasterWorks migrate an application that uses machine learning models from a SaaS provider to AWS.

When migrating on-premises MLOps to Amazon SageMaker Pipelines, customers often find it challenging to monitor metrics in training scripts and add inference scripts for custom machine learning models. Learn how Mission Cloud implemented an end-to-end SageMaker Pipeline to build the workflow of model development to production, accelerating their customer’s computer vision model production process. SageMaker Pipelines is a workflow orchestration tool for building ML pipelines with CI/CD capabilities.

Feature engineering is a critical process in which data, produced by data engineers, are consumed and transformed by data scientists to train models and improve their performance. Learn how to accelerate data processing tasks and improve collaboration between data science and data engineering teams by applying MLOps best practices from Data Reply and leveraging tools from AWS. Data Reply is focused on helping clients deliver business value and differentiation through advanced analytics and AI/ML on AWS.

Organizations realize the value of data and analytics for their businesses, but not all of them have been successful in defining a mature analytics vision and strategy. By cross-leveraging experience in process management, safeguarding data, and rich analytics practices, Genpact has developed an analytics maturity assessment framework known as the Analytics Maturity Meter (AMM). Learn how this solution evaluates a company’s current capabilities in data, process, technology, talent, and enterprise leadership.