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MLOps Lifecycle

Research work on the notion of AI and MLOPs Lifecycle.

MLOps Lifecycle Basics

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionised multiple industries, ranging from healthcare and finance to manufacturing and transportation. With the fast growth of data availability and the need for real-time decision-making, Cloud-Edge AI ML Operations (AI MLOps) has become a powerful approach, being able to combine cloud computing, edge devices, and advanced ML algorithms.

As we can see in the next figure, the life cycle is composed of three main phases: Design, Model Development and Operations. Each of which is in turn composed of three other phases, making a total of nine steps to complete the total cycle.

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The lifecycle of MLOps encompasses the end-to-end management and optimisation of ML models and workflows, integrating DevOps and data science practices. The lifecycle always begins with the problem definition and data collection, where the business goals and relevant data sources are identified. This is followed by data preprocessing, including cleaning, transformation, and feature engineering, to ensure the data is ready for modelling. The next phase involves model development, exploring and evaluating various algorithms and techniques. Once a suitable model is selected, it undergoes training and validation using historical data. After model training, the focus shifts to deployment and monitoring. The model is deployed to production environments where it interacts with real-time data, and monitoring tools are put in place to track its performance, detect anomalies, and ensure reliability.

Tools and Solutions

Phase I: Design

MLOps Step Tool Name Tool Description Link
Requirements Engineering JIRA widely used project management tool that also includes features for requirements management
Requirements Engineering Example Example Example
Requirements Engineering Example Example Example

Usage Walkthrough

External Resources


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