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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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Machine Learning Safety: Unsolved Problems

KDnuggets

There remain critical challenges in machine learning that, if left resolved, could lead to unintended consequences and unsafe use of AI in the future. As an important and active area of research, roadmaps are being developed to help guide continued ML research and use toward meaningful and robust applications.

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Building Massively Scalable Machine Learning Pipelines with Microsoft Synapse ML

KDnuggets

The new platform provides a single API to abstract dozens of ML frameworks and databases.

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The Common Misconceptions About Machine Learning

KDnuggets

Beginners in the field can often have many misconceptions about machine learning that sometimes can be a make-it-or-break-it moment for the individual switching careers or starting fresh.

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Design Patterns for Machine Learning Pipelines

KDnuggets

ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.

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Versioning Machine Learning Experiments vs Tracking Them

KDnuggets

Learn how to improve ML reproducibility by treating experiments as code.

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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.