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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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7 Libraries for Machine Learning

Analytics Vidhya

Introduction Machine learning has revolutionized the field of data analysis and predictive modelling. With the help of machine learning libraries, developers and data scientists can easily implement complex algorithms and models without writing extensive code from scratch.

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Here are 9 Must Need Machine Learning Tools for Your ML Project

Analytics Vidhya

Introduction Machine learning is a rapidly growing field that is transforming industries across sectors. It enables computers to learn from data and make predictions or decisions without being explicitly programmed.

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GitHub Actions For Machine Learning Beginners

KDnuggets

Learn how to automate machine learning training and evaluation using scikit-learn pipelines, GitHub Actions, and CML.

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How to Deploy a Machine Learning Model using Flask?

Analytics Vidhya

Introduction Deploying machine learning models with Flask offers a seamless way to integrate predictive capabilities into web applications. Flask, a lightweight web framework for Python, provides a simple yet powerful environment for serving machine learning models. appeared first on Analytics Vidhya.

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Synthetic Data for Machine Learning

KDnuggets

You don't always have high-quality labeled datasets for supervised machine learning. Learn about why you should augment your real data with synthetic data as well as the ways to generate it.

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Machine Learning Experiment Tracking Using MLflow

Analytics Vidhya

Introduction The area of machine learning (ML) is rapidly expanding and has applications across many different sectors. Keeping track of machine learning experiments using MLflow and managing the trials required to construct them gets harder as they get more complicated.