Category: Amazon Machine Learning

As customers increasingly demand personalized and efficient experiences, companies are using the power of artificial intelligence (AI) to enhance their customer support capabilities. By using the power of generative AI (GenAI), Agents for Amazon Bedrock, and the Telecom Networks API (under GSMA’s Open Gateway and Camara initiative), organizations can create intelligent chatbots and agents that […]

In the rapidly evolving life sciences industry, sales teams are facing growing challenges in engaging effectively with healthcare professionals (HCPs). With the complexity of drug portfolios, innovative go-to-market models, and intense competition, sales teams require quick access to critical data, intelligent guidance on HCP interactions, and the ability to automate administrative tasks. By addressing these […]

Introduction The utilities sector provides essential services to our homes, businesses, and communities. As the world becomes increasingly digitized, electrified, and resource-constrained, the sector faces unprecedented challenges. Three key imperatives have emerged: Modernize and expand infrastructure: Utilities must invest in a sustainable infrastructure that meets growing demand while incorporating green energy sources, reducing waste, and […]

Today, many organizations face challenges in transforming raw data into actionable insights due to the specialized skills or programming expertise required for data analysis. Recognizing this barrier, BMW Group embarked on an innovative project using Amazon Web Services (AWS) to empower its workforce and unlock the full potential of its data through generative artificial intelligence […]

Figure 1 Discovering new life-changing therapies has never been more urgent, yet the drug discovery process remains a complex and time-consuming endeavor. To accelerate breakthroughs, leading biotech and biopharma companies are turning to Biological Foundation Models (BioFMs), a type of advanced generative AI models trained on biological data. We are excited to announce that NVIDIA’s […]

With the advent of generative AI services such as Amazon Bedrock and Amazon Q Developer, it has never been easier to generate high-fidelity code while also building applications with foundation models. With this ease of use comes both great opportunity and responsibility to use this technology to impact critical barriers that exist today across digital […]

The telecommunications (telecom) industry is undergoing a transformative shift driven by advancements in artificial intelligence (AI) and machine learning (ML). Small Language Models (SLMs), which can run efficiently on Internet of Things (IoT) and edge devices, are at the forefront of this revolution. SLMs are scaled-down versions of large language models (LLMs) that deliver comparable […]

Introduction During Mergers and Acquisitions (M&A) activity, Healthcare and Life Science (HCLS) customers face additional administrative challenges integrating the technical, legal, and business systems of the target and acquirer companies. For non-technical stakeholders (business administrators, financial and legal departments, private equity parties), it is important to understand the technical challenges of integrations that will impact […]

The transition to net-zero is driving unprecedented demand for the critical minerals needed in everything from electric vehicles to wind turbines. Meeting this surging demand is leading the mining industry to a technological revolution of its own – an electrification revolution. Traditionally, mining operations have relied heavily on diesel-powered trucks, excavators, and mobile machinery to […]

Smart Manufacturing enables organizations to accumulate vast amounts of data from factory systems and operations. However, this data is often siloed across multiple formats and systems, making it challenging to extract meaningful insights to improve operational efficiency. Furthermore, as the experienced workforce retires, the vast knowledge about troubleshooting and resolving production issues is being lost. […]