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MLOps: A complete guide for building, deploying, and managing machine learning models

Data Science Dojo

MLOps practices include cross-validation, training pipeline management, and continuous integration to automatically test and validate model updates. Examples include: Cross-validation techniques for better model evaluation. Managing training pipelines and workflows for a more efficient and streamlined process.

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The Age of Health Informatics: Part 1

Heartbeat

Algorithm Development and Validation: Data scientists and machine learning engineers are responsible for developing and validating algorithms that power health informatics applications. Issues such as informed consent, data ownership, and responsible data sharing must be carefully addressed to maintain public trust.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics. Understanding how to assess model performance is crucial for data scientists. Students should learn about data validation techniques and the importance of data governance.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

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Large Language Models: A Complete Guide

Heartbeat

Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data. Some of the steps that can be taken include: Data Governance: Implementing rigorous data governance policies that ensure fairness, transparency, and accountability in the data used to train LLMs.