Remove content tag whole-person-care
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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 3

AWS Machine Learning Blog

The sample use case used for this series is a visual quality inspection solution that can detect defects on metal tags, which you can deploy as part of a manufacturing process. In a production setting, you can decide to automatically notify another system that takes care of removing faulty metal tags from the production line.

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Getting to know the new HDR

NVIDIA Developer

This post kicks off a whole series of blog posts on HDR and color evolutions for real-time graphics. Why you care From my perspective, HDR display technology really is the biggest leap forward in the quality of pixels in over 20 years. It really can be a whole new experience. Colors can both be richer and brighter.

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ACL 2022 Highlights

Sebastian Ruder

This was my first in-person conference since ACL 2019. The CL community is well positioned to advance the accessibility of scientific content and I am excited to see the progress of this grassroots initiative. ACL 2022 took place in Dublin from 22nd–27th May 2022. The KinyaBERT model architecture.

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High-quality human feedback for your generative AI applications from Amazon SageMaker Ground Truth Plus

AWS Machine Learning Blog

Large language models (LLMs) are being used in chatbots for creative pursuits, academic and personal assistants, business intelligence tools, and productivity tools. For multi-modal models, such as text-to-image or text-to-video models, the models may output content unrelated to the prompt.

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Create a Generative AI Gateway to allow secure and compliant consumption of foundation models

AWS Machine Learning Blog

For example, if Retrieval Augmented Generation (RAG)-based applications accidentally include personally identifiable information (PII) data in context, such issues need to be detected in real time. Regulatory uncertainty, especially over IP and data privacy, requires observability, monitoring, and trace of generations.

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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

Most data scientists just do not care about infrastructure. And if they do care about infrastructure, they are just MLOps engineers in training. They just need to kind of tag things like “Hey, by the way, we’re using these data sources. Aurimas: Was it content generation? They’re on the step to a new journey.

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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

Stitch Fix had a no meeting day, I set aside a whole day to think about this problem. There was a tech branding team that I was part of, which was trying to get quality content out. We had a whole system where each envelope, you would specify tags. So, plus one for work from home Wednesdays. It’s something to think about.

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