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Synthetic data generation: Building trust by ensuring privacy and quality

IBM Journey to AI blog

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning Blog

It uses machine learning–powered automatic speech recognition (ASR), automatic language identification, and post-processing technologies. In this blog post, you will learn how to power your applications with Amazon Transcribe capabilities in a way that meets your security requirements.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. However, bringing such solutions and models to the business-as-usual operations is not an easy task. Our approach applies to both open-source and proprietary models equally.

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning Blog

This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc. It is the number five cause of death according to the American Stroke Association and a leading cause of disability in the US. So why hasn’t it been used yet? Stroke victims can lose around 1.9

ML 101
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Building and Deploying CV Models: Lessons Learned From Computer Vision Engineer

The MLOps Blog

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Hopefully, at the end of this blog, you will know a bit more about finding your way around computer vision projects.

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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Google Research has been at the forefront of this effort, developing many innovations from privacy-safe recommendation systems to scalable solutions for large-scale ML. Inspired by the success of multi-core processing (e.g.,

Algorithm 110
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Large Language Models: A Complete Guide

Heartbeat

These models have the potential to revolutionize industries ranging from customer service to scientific research, but their capabilities and limitations are still not fully understood. We will also examine the challenges associated with LLMs, such as bias, privacy concerns, and ethical considerations.