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#ai-deployment

Every item tagged ai-deployment, newest first.

5 items

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Workers are spending over 6 hours a week botsitting AI, fueling job frustration

A study found workers spend over 6 hours a week on 'botsitting' — manually correcting or intervening in AI outputs. This hidden labor fuels job frustration and burnout. You may need to account for similar manual oversight costs when deploying AI in your own workflows. The study highlights the often-overlooked human labor required to maintain AI systems.

Key takeaways
  • Workers spend 6+ hours/week on manual AI correction.
  • 'Botsitting' linked to increased job frustration and burnout.
  • Hidden labor costs should be factored into AI deployment.
HHN AI RSS#ai-deployment#human-in-the-loop#labor-costs
otherOct 16

Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face

Google Cloud's C4 instances with Intel Xeon processors provide a 70% TCO improvement for running open-source GPT models compared to previous gen. This performance boost enables builders to deploy AI models more cost-effectively. Hugging Face collaborated with Google Cloud and Intel to optimize model performance. The TCO reduction can help builders scale AI deployments.

Key takeaways
  • 70% TCO improvement for open-source GPT models on C4 instances.
  • Google Cloud, Intel, and Hugging Face collaborated on performance optimization.
  • C4 instances enable cost-effective AI model deployment at scale.
HHugging Face Blog#cloud-computing#cost-optimization#open-source#ai-deployment
otherOct 23

Introducing HUGS - Scale your AI with Open Models

Hugging Face introduced HUGS, a new initiative to help scale AI with open models. HUGS provides access to a range of open models, tools, and expertise to support builders in deploying AI solutions. The initiative aims to make AI more accessible and affordable. You can explore HUGS through the Hugging Face platform.

Key takeaways
  • HUGS offers access to open models and tools for AI deployment.
  • Hugging Face provides expertise to support builders.
  • Initiative aims to make AI more accessible and affordable.
HHugging Face Blog#open-models#ai-deployment#hugging-face
otherDec 5

AMD + 🤗: Large Language Models Out-of-the-Box Acceleration with AMD GPU

AMD and Hugging Face have collaborated to enable out-of-the-box acceleration of large language models on AMD GPUs. This integration allows developers to deploy models more efficiently without requiring custom optimization. The partnership aims to make AI deployment more accessible and cost-effective for builders.

Key takeaways
  • AMD GPUs now support out-of-the-box LLMs acceleration via Hugging Face.
  • No custom optimization required for deployment.
  • Partnership targets more accessible and cost-effective AI deployment.
HHugging Face Blog#gpu-acceleration#ai-deployment#hardware
otherMay 24

Hugging Face Collaborates with Microsoft to launch Hugging Face Model Catalog on Azure

Hugging Face and Microsoft have partnered to launch the Hugging Face Model Catalog on Azure, enabling users to deploy and manage models directly on the Azure platform. This integration allows for seamless model deployment, automated workflows, and scalable model serving. Builders can now access a wide range of models and deploy them on Azure for production use. The partnership aims to simplify the process of bringing AI models to production.

Key takeaways
  • Hugging Face Model Catalog is now available on Azure.
  • Integration enables automated workflows and scalable model serving.
  • Partnership targets simplifying AI model deployment in production.
HHugging Face Blog#model-catalog#azure-integration#ai-deployment