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Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI…

ODSC - Open Data Science

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3. are harnessed to channel LLMs output.

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Uncover hidden connections in unstructured financial data with Amazon Bedrock and Amazon Neptune

AWS Machine Learning Blog

For instance, this solution can highlight that delays at a parts supplier may disrupt production for downstream auto manufacturers in a portfolio though none are directly referenced. Overall, this prototype demonstrates the art of possible with knowledge graphs and generative AI—deriving signals by connecting disparate dots.

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Generating fashion product descriptions by fine-tuning a vision-language model with SageMaker and Amazon Bedrock

AWS Machine Learning Blog

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. jpg and the complete metadata from styles/38642.json. From here, we can fetch the image for this product from images/38642.jpg

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Optimize your machine learning deployments with auto scaling on Amazon SageMaker

AWS Machine Learning Blog

SageMaker supports automatic scaling (auto scaling) for your hosted models. Auto scaling dynamically adjusts the number of instances provisioned for a model in response to changes in your inference workload. When the workload increases, auto scaling brings more instances online.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. Is it accessible from your language/framework/infrastructure, framework, or infrastructure? Can you render audio/video?

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Google Research, 2022 & beyond: Research community engagement

Google Research AI blog

We also support Responsible AI projects directly for other organizations — including our commitment of $3M to fund the new INSAIT research center based in Bulgaria. Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M

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Synthetic Data: A Model Training Solution

Viso.ai

1: Variational Auto-Encoder. A Variational Auto-Encoder (VAE) generates synthetic data via double transformation, known as an encoded-decoded architecture. Block diagram of Variational Auto-Encoder (VAE) for generating synthetic images and data – source. Technique No.1: Then, it decodes this data back into simulated data.