Author name: aiepicentre

Build a Multi-Agent System with LangGraph and Mistral on AWS

Amazon Bedrock, Amazon Machine Learning, Foundation models, Generative AI, Technical How-to /

Agents are revolutionizing the landscape of generative AI, serving as the bridge between large language models (LLMs) and real-world applications. These intelligent, autonomous systems are poised to become the cornerstone of AI adoption across industries, heralding a new era of human-AI collaboration and problem-solving. By using the power of LLMs and combining them with specialized […]

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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AI/ML, Amazon Bedrock, Amazon Bedrock Agents, Amazon Machine Learning, Generative AI /

AI agents continue to gain momentum, as businesses use the power of generative AI to reinvent customer experiences and automate complex workflows. We are seeing Amazon Bedrock Agents applied in investment research, insurance claims processing, root cause analysis, advertising campaigns, and much more. Agents use the reasoning capability of foundation models (FMs) to break down

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Building a virtual meteorologist using Amazon Bedrock Agents

Amazon Bedrock, Amazon Bedrock Agents, Amazon Cognito, AWS Amplify, AWS Lambda, Generative AI, Intermediate (200) /

The integration of generative AI capabilities is driving transformative changes across many industries. Although weather information is accessible through multiple channels, businesses that heavily rely on meteorological data require robust and scalable solutions to effectively manage and use these critical insights and reduce manual processes. This solution demonstrates how to create an AI-powered virtual meteorologist

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Create a virtual stock technical analyst using Amazon Bedrock Agents

Advanced (300), Amazon Bedrock, Amazon Bedrock Agents, Financial Services, Generative AI /

Stock technical analysis questions can be as unique as the individual stock analyst themselves. Queries often have multiple technical indicators like Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), and others. Answering these varied questions would mean writing complex business logic to unpack the query into parts and fetching the necessary

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Build a multimodal social media content generator using Amazon Bedrock

Amazon Bedrock, Content Production, Generative AI, Technical How-to /

In today’s digital age, social media has revolutionized the way brands interact with their consumers, creating a need for dynamic and engaging content that resonates with their target audience. There’s growing competition for consumer attention in this space; content creators and influencers face constant challenges to produce new, engaging, and brand-consistent content. The challenges come

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Build an ecommerce product recommendation chatbot with Amazon Bedrock Agents

AI/ML, Amazon Bedrock, Artificial Intelligence, Generative AI, Industries, Intermediate (200), Retail, Technical How-to /

Many ecommerce applications want to provide their users with a human-like chatbot that guides them to choose the best product as a gift for their loved ones or friends. To enhance the customer experience, the chatbot need to engage in a natural, conversational manner to understand the user’s preferences and requirements, such as the recipient’s

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AWS DeepRacer enables builders of all skill levels to upskill and get started with machine learning

Amazon Machine Learning, AWS DeepRacer, Sports /

In today’s technological landscape, artificial intelligence (AI) and machine learning (ML) are becoming increasingly accessible, enabling builders of all skill levels to harness their power. As more companies adopt AI solutions, there’s a growing need to upskill both technical and non-technical teams in responsibly expanding AI usage. Getting hands-on experience is crucial for understanding and

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Transform customer engagement with no-code LLM fine-tuning using Amazon SageMaker Canvas and SageMaker JumpStart

Advanced (300), Amazon SageMaker, Amazon SageMaker Canvas, Amazon SageMaker JumpStart, Artificial Intelligence /

Fine-tuning large language models (LLMs) creates tailored customer experiences that align with a brand’s unique voice. Amazon SageMaker Canvas and Amazon SageMaker JumpStart democratize this process, offering no-code solutions and pre-trained models that enable businesses to fine-tune LLMs without deep technical expertise, helping organizations move faster with fewer technical resources. SageMaker Canvas provides an intuitive

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Improve accuracy of Amazon Rekognition Face Search with user vectors

Advanced (300), Amazon Machine Learning, Amazon Rekognition /

In various industries, such as financial services, telecommunications, and healthcare, customers use a digital identity process, which usually involves several steps to verify end-users during online onboarding or step-up authentication. An example of one step that can be used is face search, which can help determine whether a new end-user’s face matches those associated with

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Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

Advanced (300), Amazon Bedrock, Amazon Redshift, Generative AI, Technical How-to /

With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across

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