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Enhancing Data Fabric with SQL Asset Type in IBM Knowledge Catalog

IBM Data Science in Practice

In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption. This involves enhancing the metadata associated with each data point, such as adding tags, descriptions, and quality metrics.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning Blog

Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more. strip() for item in response.strip().split("nn")[1:-1]

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Accelerate release lifecycle with pathway to deploy: Part 2

IBM Journey to AI blog

Given enterprise complexity, the most difficult part of this stage is the automation of testing capabilities (wherein test data preparation and execution of test cases across multiple systems is mostly semi-automated). This holistic approach represents a significant leap in operational efficiency and risk mitigation for the enterprise.

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What Is a Transformer Model?

Hacker News

Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago. That’s a radical shift from a 2017 IEEE study that reported RNNs and CNNs were the most popular models for pattern recognition. appeared first on NVIDIA Blog.

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Beyond prompting: getting production quality LLM performance with Snorkel Flow

Snorkel AI

However, as enterprises begin to look beyond proof-of-concept demos and toward deploying LLM-powered applications on business-critical use cases, they’re learning that these models (often appropriately called “ foundation models ”) are truly foundations, rather than the entire house. mildew|odor|stain|dust|fade|UV|weather|moisture|water).{,30}resistant”);

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Beyond prompting: getting production quality LLM performance with Snorkel Flow

Snorkel AI

However, as enterprises begin to look beyond proof-of-concept demos and toward deploying LLM-powered applications on business-critical use cases, they’re learning that these models (often appropriately called “ foundation models ”) are truly foundations, rather than the entire house. mildew|odor|stain|dust|fade|UV|weather|moisture|water).{,30}resistant”);

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Explosion in 2022: Our Year in Review

Explosion

We’ve published several technical blog posts and reports, and created a bunch of new videos covering many tips and tricks to get the most out of our developer tools. Edi, Lj and team have written a comprehensive blog post covering full details of the spancat implementation as well as an architecture case study on nested NER.

Python 59