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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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How to Report and Analyze Like a Pro: 10 Best Practices for Reporting and Analytics in a Contact Center

NobelBiz

With its intuitive interface and buil-in analytics and reporting engine, it is the go-to solution for contact centers to improve their efficiency, and ensure the accuracy and exactitude f collected data. The following are 10 of the best practices to ensure the accuracy and the proper handling of reporting and analytics: 1.

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Talkdesk Infrastructure Security

Talkdesk

The Engineering Security teams work closely with Site Reliability Engineering teams to remediate or mitigate any cloud infrastructure configuration risks that are found in our AWS environments. with an industry standard ECDHE-RSA-AES128-SHA256 cipher. For data in transit, we use TLS 1.2

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Best practices to build generative AI applications on AWS

AWS Machine Learning

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. This enables Philips ML engineers and developers to provide updates, bug fixes, and future enhancements without disrupting the entire system.

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Improving your LLMs with RLHF on Amazon SageMaker

AWS Machine Learning

Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks.

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Website Redesign Services: Importance and Benefits

OctopusTech

Redesigning also includes search engine optimization, optimizing website speed, monitoring technical performance, and responsive web design. We believe that you should design it to meet the latest industry standards, organizational goals, and the needs of customers. When Should You Redesign Your Website?