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Streamlit vs Gradio – A Guide to Building Dashboards in Python

Analytics Vidhya

Introduction Machine Learning is a fast-growing field, and its applications have become ubiquitous in our day-to-day lives. As the demand for ML models increases, so makes the demand for user-friendly interfaces to interact with these models.

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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

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Google Announces ML-Powered Abuse Detection Dashboard to Enhance API Security

Analytics Vidhya

Recently, Google announced the addition of an abuse detection dashboard. The dashboard is driven by cutting-edge machine learning algorithms and made for its Apigee API management service.

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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

Why companies are turning to specialized machine learning tools like MLflow. A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. Image by Matei Zaharia; used with permission.

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Create Interactive Dashboards with Streamlit and Python

Analytics Vidhya

Introduction In Data Visualization, Dashboard is the great Graphical User Interfaces that. The post Create Interactive Dashboards with Streamlit and Python appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. Machine learning developers are beginning to look at an even broader set of risk factors. Sources of model risk.

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Choosing the right Machine Learning Framework

Domino Data Lab

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. How to choose the right ML Framework.