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MLOps principles

MLOps, or Machine Learning Operations, encompasses a set of principles that guide the efficient design, deployment, and maintenance of machine learning models. Understanding these principles is crucial for elevating MLOps projects to the next level.

Key principles of MLOps include versioning, testing, automation, monitoring and tracking, and reproducibility. Versioning ensures that different iterations of models and datasets are managed effectively, allowing for easy rollback and comparison. Testing is essential to validate model performance and ensure reliability before deployment.

Automation plays a significant role in streamlining workflows, reducing manual intervention, and enhancing efficiency. Monitoring and tracking involve continuously observing model performance in production to identify issues and ensure optimal functioning. Lastly, reproducibility ensures that models can be consistently replicated, which is vital for validation and compliance purposes.

For a deeper understanding of these principles, it is recommended to explore beginner-friendly tutorials that provide practical examples and detailed explanations of each concept 4.

Understanding MLOps

 Analytics Vidhya

What is MLOps? How to do MLOps? Machine learning operations

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A Gentle Introduction to MLOps

 Towards Data Science

In this article, we will learn what is MLOps or Machine Learning Operations. I will try to simplify the vast and intriguing world of ML Operations and its associated infrastructure. This article is…

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A Simple Explanation of MLOps

 Towards Data Science

Finally! The company you’ve been working for, for the past 8 years has heeded your cries to invest in machine learning - a growing feat poised to revolutionize business as we know it. You were placed…...

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Introduction to MLOps Principles

 Towards Data Science

If you’re looking to elevate your MLOps projects to the next level, understanding its principles is an essential part of the process. In this article, we’ll offer an introduction to MLOps principles a...

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MLOps: The Ultimate Guide

 Towards Data Science

You’ll find conflicting definitions on MLOps: Is it a movement, a philosophy, a platform, or a job title? Most are either far too vague — a “philosophy” — or far too specific, just referring to one…

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MLOps: an Easy Explanation

 Python in Plain English

In today’s data-driven world, machine learning (ML) has emerged as a powerful tool for extracting insights and making predictions from vast amounts of data. Yet, the journey from development to deploy...

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How (not) to do MLOps

 Towards Data Science

I presented the paper "Using AntiPatterns to avoid MLOps Mistakes" in one of our meets, here's the gist.

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Fundamentals of MLOps — Part 1 | A Gentle Introduction to MLOps

 Analytics Vidhya

In this first post of the Fundamentals of MLOps series, we start with DevOps & proceed to understand the key principles & practices of MLOps

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What is MLOps — Everything You Must Know to Get Started

 Towards Data Science

Until recently, all of us were learning about software development lifecycle(SDLC) and how it goes from requirement elicitation → designing → development → testing → deployment → all the way down to…

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Navigating MLOps

 Towards Data Science

Data Science in production in 2022 Continue reading on Towards Data Science

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Get started with MLOps

 Towards Data Science

Getting machine learning (ML) models into production is hard work. In this tutorial I'll go over my personal thoughts (with implementation examples) on principles suitable for the journey of putting M...

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A Beginner-Friendly Introduction to MLOps

 Towards Data Science

A first step to MLOps Photo by Sarah Dorweiler on Unsplash I have been interested in MLOps for a while now. I first learned about it from machine learning engineers, and as I was a PhD student at the...

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