Remove solutions learning-and-development
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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The Amazon D&C team implemented the solution in a pilot for Amazon engineers and collected user feedback. In this post, we share how we analyzed the feedback data and identified limitations of accuracy and hallucinations RAG provided, and used the human evaluation score to train the model through reinforcement learning.

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Introducing automatic training for solutions in Amazon Personalize

AWS Machine Learning

Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. After you finish importing your data, you are ready to create a solution.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning

A multimodal embeddings model is designed to learn joint representations of different modalities like text, images, and audio. By training on large-scale datasets containing images and their corresponding captions, a multimodal embeddings model learns to embed images and texts into a shared latent space.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in. This post is co-written with Jayadeep Pabbisetty, Sr.

APIs 101
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[Case Study]: Multilingual Support Fuels Gaming Company’s Global Expansion

When support needs peaked for video game developer Wooga, they brought in multilingual player support from ModSquad. Learn how this customized solution helped them save money, grow their player base, and free up more time to create compelling, engaging games.

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. Machine learning operations (MLOps) applies DevOps principles to ML systems.

Analytics 106
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ISO 42001: A new foundational global standard to advance responsible AI

AWS Machine Learning

At AWS, we remain committed to harnessing AI responsibly, working hand in hand with our customers to develop and use AI systems with safety, fairness, and security at the forefront. It establishes a framework for organizations to systematically address and control the risks related to the development and deployment of AI.

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Everyone Is Accountable and Responsible for a Great Customer Experience!

Speaker: Bryan Horn, Founder, CS Solutions

Join Bryan Horn, author of The Customer Service Revolution and founder of CS Solutions, and learn how to master the warm handoff. Bryan will teach how to develop a culture of accountability so that all members of the organization are equipped to handle customer concerns and offer quick resolutions.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.