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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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5 Upcoming DS/ML/AI/DE Online Conferences in 2021

Analytics Vidhya

The post 5 Upcoming DS/ML/AI/DE Online Conferences in 2021 appeared first on Analytics Vidhya. ArticleVideo Book Introduction The value of meeting people and interacting with people in a private space is one of the best ways to network.

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ML Trends for Solving Business Intelligence Problems

Analytics Vidhya

Introduction In September 2021, Gartner released a separate report on. The post ML Trends for Solving Business Intelligence Problems appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon.

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Top 14 Artificial Intelligence Startups to watch out for in 2021!

Analytics Vidhya

Overview Reducing company costs, generating customer insights & intelligence, and improving customer experiences are the three most popular ML and AI use cases Here. The post Top 14 Artificial Intelligence Startups to watch out for in 2021! appeared first on Analytics Vidhya.

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Racing into the future: How AWS DeepRacer fueled my AI and ML journey

AWS Machine Learning Blog

At the time, I knew little about AI or machine learning (ML). But AWS DeepRacer instantly captured my interest with its promise that even inexperienced developers could get involved in AI and ML. Panic set in as we realized we would be competing on stage in front of thousands of people while knowing little about ML.

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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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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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