mlops

MLOps, or Machine Learning Operations, is a discipline that integrates machine learning (ML) with operational practices to streamline the entire ML lifecycle. It encompasses the processes of developing, deploying, and maintaining ML models in production environments efficiently and reliably. MLOps draws from principles of DevOps, emphasizing collaboration, automation, and continuous monitoring. As organizations increasingly adopt ML technologies, MLOps becomes essential for ensuring that models perform optimally and adapt to changing data and requirements. By bridging the gap between data science and operations, MLOps facilitates the successful implementation of ML solutions across various applications and industries.

What is MLOps

Towards AI

Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. Thus, MLOps is the intersection of Machine ...

ML Ops: Machine Learning as an Engineering Discipline

Towards Data Science

ML Ops (or MLOps) is a set of practices uniting Machine Learning, DevOps and Data Engineering, aiming to deploy and maintain ML systems reliably and efficiently.

What’s MLOps?

Towards Data Science

Machine Learning (ML) IT Operations (Ops) aims to apply the engineering culture and practices promoted by DevOps to ML systems. But why? Data science and ML are becoming the table stakes for solving…

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...

ML-OPS Guide Series- 1

Towards AI

To put it in simple terms, MLOps or ML Ops is a set of practices that aims to deploy and maintain ML models in production reliably and efficiently from defining the scope(problem statement) of the…

Machine Learning Operations (MLOps) For Beginners

Towards Data Science

End-to-end Project Implementation Image created by the author Developing, deploying, and maintaining machine learning models in production can be challenging and complex. This is where Machine Learni...

MLOps: DevOps fancier cousin

Analytics Vidhya

ML Models go through multiple iterations in the form of experiments, with data scientists training them on one set of data and testing them on others. Machine Learning Operations(MLOps) is the…

Machine Learning Orchestration vs MLOps

Towards Data Science

Photo by Mark Williams on Unsplash There is a saying that I’ve heard from the ML engineers that I’ve worked with that “most of machine learning operations (MLOps) is just data engineering”. There is a...

Enabling MLOPs in Three Simple Steps

Towards Data Science

With DBT Core, Kedro, and Weights & Biases Image by Author I recently engaged in a project involving the implementation of a multiclass classification prediction system utilising financial transactio...

MLOps is Just the Tip of the Iceberg — Key Elements You Need Before Starting

Towards AI

Unleashing the Power of MLOps: A Journey of Continuous Improvement Imagine a world where every machine runs without errors, where people aim for quality as a goal and as a way of living. In today’s da...

MLOps (Machine Learning Operations)

Python in Plain English

Image by author In this article, we are going to learn about MLOps. whenever We talk about machine learning projects or data science projects, you know the life cycle of data science projects from dat...

🫀MLOPs recap, part 1

TheSequence

💡 ML Concept of the Day: What is MLOps? One of the challenges to understanding MLOps is that the term itself is used very loosely in the ML community. In general, we should think about MLOps as an ex...