Digital Alpha Platforms

Developers guide to using Amazon EFS with Amazon ECS and AWS Fargate. Amazon ECS is a fully managed container orchestrator service purpose-built for the cloud and integrated with other AWS services. ECS supports deploying containers (wrapped in so called tasks) on both Amazon EC2 and AWS Fargate. Amazon EFS is a fully managed, elastic, shared file system designed to be consumed by other AWS services, such as ECS, or EC2 instances. Amazon EFS scales transparently, replicates your data, and makes it available across Availability Zones and supports multiple storage tiers to meet the demands of the majority of workloads. This integration works for all ECS customers using either EC2 instances or Fargate. This integration has been enabled for Fargate via platform version 1.4, which we have recently released. Before we start, it is important to call out that this integration is orchestrator-specific because it is an Amazon ECS task-level configuration that applies to both ECS on EC2 as well as ECS on Fargate. Amazon EKS has different integration mechanisms for EFS. For EKS on EC2, you can refer to this link in the EKS documentation. For EKS on Fargate, you can track this GitHub roadmap item because, at the time of this writing, the integration is still being worked on. Know more: https://lnkd.in/gYaZyt5K

RIA firms often struggle to identify what’s truly driving growth. Advisors are working hard—meeting clients, running campaigns, and making calls—but: - Are they prioritizing the right leads? - Which channels are delivering measurable ROI? - How can performance be optimized? Without actionable insights, firms risk inefficiencies, missed opportunities, and wasted resources. Using AI-powered solutions with #AmazonQ Business, RIA firms can: 1️⃣ Gain Advisor-Level Visibility: Track performance metrics like leads generated, conversion rates, and outreach efforts. 2️⃣ Optimize ROI: Identify high-performing channels like referrals or digital ads to allocate resources effectively. 3️⃣ Unify Tools: Integrate platforms like Salesforce, Redtail and more into one seamless dashboard for complete transparency. With streamlined data and actionable insights, RIA firms can enhance advisor productivity, improve decision-making, and focus on scaling smarter. Looking to turn data into a competitive edge? Learn more about what Amazon Q Business can do for you: https://lnkd.in/g_uwMC7P AWS for Financial Services Andrew Godfrey Bhavye Sharma #AWSPartner #Financialplanning #FinancialAdvisors #GenerativeAI

Unlock the power of Retrieval-Augmented Generation (RAG) and create your own Generative AI chatbot in just 20 minutes! This step-by-step tutorial demonstrates how to leverage Amazon Bedrock Knowledge Base to build a cutting-edge generative AI chatbot. Learn more: https://lnkd.in/g56wZ7Gb

Financial firms often face challenges with unstructured data—manuals, communications, and documents in formats like Word, PDFs, Excel, and PowerPoint—scattered across platforms like SharePoint, OneNote, and internal intranets. This fragmentation hampers efficient search and decision-making. AI-driven solutions like #AmazonQ Business and QnABot on AWS help financial institutions: - Centralize Data Access: Unify disparate data sources for seamless accessibility. - Automate Workflows: Streamline compliance, RFP responses, and reporting to reduce manual tasks. - Enhance Decision-Making: Deliver real-time, role-based insights to empower strategic choices. Principal Financial Group reduced manual search time by 50% with an AI-powered internal chatbot, enabling teams to focus on high-value decisions. Empower your firm with AI-driven solutions to enhance productivity and streamline operations: https://lnkd.in/dnkeEWza AWS for Financial Services #AWSPartner #GenerativeAI #Financialplanning #FinancialAdvisors #AWS

Amazon Bedrock’s multi-agent collaboration enables developers to build, deploy, and manage multiple specialized agents that work together seamlessly to tackle complex workflows. Each agent focuses on a specific task under the coordination of a supervisor agent, which breaks down processes into manageable steps, assigns tasks to the appropriate agents, and ensures accurate, and reliable outcomes. With built-in tools like the AWS Console, debugging and tracing capabilities, and automatic routing, developers can set up workflows in minutes without requiring custom coding. This capability allows workflows to scale and adapt as business needs evolve, eliminating the challenges of ad-hoc solutions and ensuring workflows remain efficient and aligned with enterprise goals. By handling operational tasks, Amazon Bedrock allows teams to focus on innovation and deliver meaningful business value. Know more: https://lnkd.in/gvMC33Ea

For #SMEs, keeping product documentation accurate and up-to-date can feel like an uphill battle. #AmazonQ Business changes the game by automating initial drafts using your product requirements, style guides, and templates—all while maintaining consistency and accuracy. - Save 10–20% of documentation time. - Deliver professional, error-free content faster. - Free your team to focus on growth, not repetitive tasks. Organizations have successfully used #AmazonQ Business to streamline extensive help documentation updates for new product launches—reducing time and ensuring consistent, accurate results. Learn how businesses are transforming workflows and driving efficiency with Amazon Q Business: https://lnkd.in/dxB6H9gv #AWSPartner #GenerativeAI #FinancialServices #Automation

Llama 3.3 70B from Meta is now available on AWS, offering more options for building generative AI applications. Meta’s most advanced large language model (LLM) gives AWS customers more choice when building, deploying, and scaling generative AI applications. The latest model from technology company Meta—Llama 3.3 70B—is now available in Amazon Bedrock and Amazon SageMaker AI, as well as via Amazon Elastic Compute Cloud (Amazon EC2) using AWS Trainium and Inferentia, and represents advancements in both model efficiency and performance optimization. The new, text-only model offers improvements in reasoning, math, general knowledge, instruction following, and tool use in comparison to the earlier version. Further, Llama 3.3 70B delivers similar performance to Llama 3.1 405B, while requiring only a fraction of the computational resources. Know more: https://lnkd.in/dBxbMbiv

New state-of-the-art foundation models from Amazon deliver frontier intelligence and industry-leading price performance. As the next step in their AI journey, Amazon has built Amazon Nova, a new generation of foundation models (FMs). With the ability to process text, image, and video as prompts, customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or generate videos and other multimedia content. “Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” said Rohit Prasad, SVP of Amazon Artificial General Intelligence. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.” Know more: https://lnkd.in/g4Z4hU9K

Retrieval-Augmented Generation (RAG) often stands out as a foundational capability across many LLM-driven solutions, striking a balance between ease of implementation and real-world impact. By combining a retriever that surfaces relevant documents with an LLM that synthesizes responses, RAG streamlines knowledge access, making it invaluable for applications like customer support, research, and internal knowledge management. Defining clear evaluation criteria is a critical step in ensuring LLM solutions perform up to a required standard. Just as test-driven development ensures software reliability, an evaluation-driven approach helps organizations validate and improve their AI workflows by establishing clear & measurable success criteria, which are essential for handling open-ended responses. For RAG, an evaluation set typically consists of Q&A pairs that represent the types of questions end users are likely to ask. These pairs are often generated from a subset of documents, such as those that are most viewed or frequently accessed, ensuring the evaluation focuses on the most relevant content. https://lnkd.in/gchyyh8K

#Investment advisory firms: What if your clients could answer their own questions—instantly and seamlessly? Imagine this: A client wonders, “How’s my account performance this quarter compared to my target plan?” Instead of calling or waiting for a response, they log into your client portal, type the question, and instantly receive a clear, data-driven answer. No emails. No delays. Just self-service simplicity—delivered through the portal they already trust. #AmazonQ Business changes the game: With AI-powered integration, Amazon Q Business consolidates your tools into a single, intuitive solution. Even better, Amazon Q-powered widgets can be seamlessly embedded into your client portal, providing clients with instant, actionable insights right at their fingertips. How It Works: 1. Embedded Widgets: Easily integrate Amazon Q-powered widgets into your client portal, connecting systems like Wealthbox (CRM), Orion (portfolio performance), and OneDrive (Investment Policy Statements). 2. Real-Time Insights: Clients can query their own data—like account performance or plan comparisons—and receive instant, actionable responses. 3. Enhanced Client Experience: Provide transparency and accessibility while freeing your team to focus on high-value advisory work. Example of What Clients Can Do: Ask: “How’s my account performing this quarter compared to my target plan?” Get: A clear, comprehensive answer in seconds—directly through the client portal. Smarter Tools for Better Client Outcomes With Amazon Q Business, investment advisors aren’t just delivering insights—they’re empowering clients with real-time access to their financial information. By embedding widgets directly into your client portal, you ensure a seamless, user-friendly experience that enhances transparency, builds trust, and strengthens client relationships. Ready to transform how your clients interact with their data? Learn how Amazon Q Business helps investment advisory firms deliver an unparalleled client experience - https://lnkd.in/g_uwMC7P Andrew Godfrey Bhavye Sharma AWS for Financial Services #financialplanning #financialsdvisors #AWSPartner #GenerativeAI #wealthmanagement