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Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

AWS Machine Learning - AI

We start off with a baseline foundation model from SageMaker JumpStart and evaluate it with TruLens , an open source library for evaluating and tracking large language model (LLM) apps. These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing images and videos.

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Employee Spotlight: Mark Richman

Linux Academy

Mark’s passion for learning and self-sufficiency began early on in life. . But don’t worry, while Mark was learning programming languages and jamming out to the smooth sound of trumpets, he made time for the math club as well. Mark Richman, AWS Training Architect. The early, nerdy years. But how exactly did Mark get here?

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Review of Industrial IoT Solutions – Part I

Perficient

Introduction. Edge computing and more generally the rise of Industry 4.0 delivers tremendous value for your business. Having the right data strategy is critical to get access to the right information at the right time and place. Solution Overview. At the core of Industry 4.0 Configuration can also be pushed backed to the sites post-analysis.

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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

AWS Machine Learning - AI

These tables house complex domain-specific schemas, with instances of nested tables and multi-dimensional data that require complex database queries and domain-specific knowledge for data retrieval. As a result, NL2SQL solutions for enterprise data are often incomplete or inaccurate.

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Fine-tune large language models with reinforcement learning from human or AI feedback

AWS Machine Learning - AI

Supervised learning can help tune LLMs by using examples demonstrating some desired behaviors, which is called supervised fine-tuning (SFT). This method is called reinforcement learning from human feedback ( Ouyang et al. This leads to responses that are untruthful, toxic, or simply not helpful to the user. Recently, Lee et al.