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How AI/ML Technology Integration Will Help Business in Achieving Goals in 2022

KDnuggets

AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.

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Top AI and ML Conferences in 2022

Analytics Vidhya

The post Top AI and ML Conferences in 2022 appeared first on Analytics Vidhya. This is why conferences that revolve around Artificial Intelligence (AI) are great for developers, analysts and students who wish to work with AI (build or incorporate).

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Cloud ML In Perspective: Surprises of 2021, Projections for 2022

KDnuggets

Let’s take a closer look on Cloud ML market in 2021 in retrospective (with occasional drills into realities of 2020, too). Read this in-depth analysis.

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Analytics Vidhya’s Top 10 Machine Learning Blogs in 2022

Analytics Vidhya

Introduction Though machine learning isn’t a relatively new concept, organizations are increasingly switching to big data and ML models to unleash hidden insights from data, scale their operations better, and predict and confront any underlying business challenges.

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Stable Diffusion with Core ML on Apple Silicon

Machine Learning Research at Apple

Today, we are excited to release optimizations to Core ML for Stable Diffusion in macOS 13.1 Figure 1: Images generated with the prompts, "a high quality photo of an astronaut riding a (horse/dragon) in space" using Stable Diffusion and Core ML + diffusers running on-device on Apple Silicon.}> and iOS 16.2,

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Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.

ML @ CMU

Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions.

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Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership

KDnuggets

In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.