<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Machine Learning on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/machine-learning/</link><description>Recent content in Machine Learning on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sat, 27 Apr 2024 10:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/machine-learning/atom.xml" rel="self" type="application/rss+xml"/><item><title>What Is MLOps</title><link>https://www.chenshaowen.com/en/blog/what-is-mlops.html</link><pubDate>Sat, 27 Apr 2024 10:00:00 +0000</pubDate><atom:modified>Sat, 27 Apr 2024 10:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/what-is-mlops.html</guid><description>1. What Is MLOps MLOps is short for Machine Learning Operations, and it describes the standardization and engineering of the entire lifecycle around model development.
MLOps includes the following key steps:
Data management: storing, accessing, cleaning, and transforming data Model development: algorithm development and model construction Model training and tuning: training models on data, adjusting hyperparameters to optimize the model, and fine-tuning models Model evaluation: testing a model&amp;rsquo;s accuracy, generalization ability, and performance metrics Model deployment: deploying the model to the target environment and converting the model Model monitoring: monitoring the model&amp;rsquo;s behavior, performance degradation, data drift, and robustness Another similar concept is ModelOps.</description><dc:creator>微信公众号</dc:creator><category>Machine Learning</category><category>MLOps</category><category>LLM</category><category>R&amp;D</category><category>What Is</category></item><item><title>AI Fundamentals</title><link>https://www.chenshaowen.com/en/blog/ai-basic-knowledge.html</link><pubDate>Fri, 18 Aug 2023 11:22:55 +0000</pubDate><atom:modified>Fri, 18 Aug 2023 11:22:55 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/ai-basic-knowledge.html</guid><description>1. Keywords Machine Learning (ML)
The technology of automatically acquiring knowledge from data.
Neural Network (NN)
A model that imitates the structure and learning mechanism of biological neural networks; one of the branches of machine learning.
The structure of a neural network consists of an input layer, hidden layers, and an output layer.</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>LLM</category><category>Machine Learning</category><category>Knowledge Points</category><category>Learning</category></item><item><title>Interactive Notebook - Jupyter</title><link>https://www.chenshaowen.com/en/blog/interactive-notebook-jupyter.html</link><pubDate>Mon, 25 Dec 2017 17:22:45 +0000</pubDate><atom:modified>Mon, 25 Dec 2017 17:22:45 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/interactive-notebook-jupyter.html</guid><description>1. Introduction Jupyter Notebook (formerly IPython notebook) is an interactive notebook that supports running more than 40 programming languages. Jupyter Notebook is in fact a web application that lets you create and share program documents, with support for live code, mathematical equations, visualizations, and Markdown. Its uses include data cleaning</description><dc:creator>微信公众号</dc:creator><category>Tools</category><category>Python</category><category>Notes</category><category>Jupyter</category><category>Learning</category><category>R&amp;D</category><category>Data Analysis</category><category>Machine Learning</category><category>Visualization</category></item></channel></rss>