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Rebuilding Netflix Video Processing Pipeline with Microservices

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Liwei Guo, Anush Moorthy, Li-Heng Chen, Vinicius Carvalho, Aditya Mavlankar, Agata Opalach, Adithya Prakash, Kyle Swanson, Jessica Tweneboah, Subbu Venkatrav, Lishan Zhu This is the first blog in a multi-part series on how Netflix rebuilt its video processing pipeline with microservices, so we can maintain our rapid pace of innovation and continuously improve the system for …

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Breaking down the advantages and disadvantages of artificial intelligence

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Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. For example, learning, reasoning, problem-solving, perception, language understanding and more. Instead of relying on explicit instructions from a programmer, AI systems can learn from …

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

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This post is co-written with Jayadeep Pabbisetty, Sr. Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust …

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Document AI Custom Extractor, powered by gen AI, is now Generally Available

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Today, we are excited to announce that the Document AI Custom Extractor, powered by generative AI, is Generally Available (GA), open to all customers, and ready for production use through APIs and the Google Cloud Console. The Custom Extractor, built with Google’s foundation models, helps parse data from structured and unstructured documents quickly and with …

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How Generative AI Is Redefining the Retail Industry

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Ninety-eight percent of retailers plan to invest in generative AI in the next 18 months, according to a new survey conducted by NVIDIA. That makes retail one of the industries racing fastest to adopt generative AI to ramp up productivity, transform customer experiences and improve efficiency. Early deployments in the retail industry include personalized shopping …

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The importance of data ingestion and integration for enterprise AI

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The emergence of generative AI prompted several prominent companies to restrict its use because of the mishandling of sensitive internal data. According to CNN, some companies imposed internal bans on generative AI tools while they seek to better understand the technology and many have also blocked the use of internal ChatGPT. Companies still often accept …

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Inference Llama 2 models with real-time response streaming using Amazon SageMaker

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With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a …

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Getting started with AI notebooks and Jupyterhub on GKE

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The recent growth in distributed, compute-intensive ML applications has prompted data scientists and ML practitioners to find easy ways to prototype and develop their ML models. Running your Jupyter notebooks and JupyterHub on Google Kubernetes Engine (GKE) can provide a way to run your solution with security and scalability built-in as core elements of the …

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Responsible AI at Google Research: User Experience Team

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Posted by Ayça Çakmakli, UX Lead, Google Research, Responsible AI and Human Centered Technology Team Google’s Responsible AI User Experience (Responsible AI UX) team is a product-minded team embedded within Google Research. This unique positioning requires us to apply responsible AI development practices to our user-centered user experience (UX) design process. In this post, we …

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