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The Data Disconnect: A Key Challenge for Machine Learning Deployment

insideBIGDATA

This article is excerpted from the book, "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment," by Eric Siegel, Ph.D., with permission from the publisher, MIT Press.

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Why Mathematics is Essential for Data Science and Machine Learning

insideBIGDATA

Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.

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2023 ML Pulse Report: The Latest Trends and Challenges in Machine Learning

insideBIGDATA

Our friends over at Sama recently published a comprehensive report on the potential and challenges of AI as reported by Machine Learning professionals.

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Deploying Machine Learning Models at Scale: Strategies for Efficient Production

insideBIGDATA

In this contributed article, freelance writer Ainsley Lawrence briefly explores deploying machine learning models, showing you how to manage multiple models, establish robust monitoring protocols, and efficiently prepare to scale.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.

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10 Must Read Machine Learning Research Papers

Analytics Vidhya

Introduction In this article, we dive into the top 10 publications that have transformed artificial intelligence and machine learning. We’ll take you through a thorough examination of recent advancements in neural networks and algorithms, shedding light on the key ideas behind modern AI.

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Comparing Scikit-Learn and TensorFlow for Machine Learning

Machine Learning Mastery

Choosing a machine learning (ML) library to learn and utilize is essential during the journey of mastering this enthralling discipline of AI. Understanding the strengths and limitations of popular libraries like Scikit-learn and TensorFlow is essential to choose the one that adapts to your needs.