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MLOps principles

MLOps, or Machine Learning Operations, encompasses a set of principles designed to streamline the development, deployment, and maintenance of machine learning models. Understanding these principles is crucial for elevating MLOps projects to a higher level of efficiency and effectiveness.

Key principles of MLOps include versioning, which ensures that different iterations of models and datasets are tracked; testing, which validates the performance and reliability of models; and automation, which facilitates the deployment and monitoring processes. Additionally, monitoring and tracking are essential for assessing model performance in real-time, while reproducibility ensures that results can be consistently replicated across different environments 4.

These principles collectively contribute to a structured workflow that encompasses the entire lifecycle of machine learning projects, from problem definition to data engineering, model engineering, and code engineering 4.

If you’re new to MLOps, starting with beginner-friendly tutorials can help you grasp these concepts more effectively.

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

MLOps has established itself as an independent field in the intersection of machine learning, data science, software engineering and (cloud) infrastructure. In this post, I want to take a look at…

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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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