MLOps Lifecycle

Research work on the notion of AI and MLOps Lifecycle.

The 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.

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.

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	https://www.atlassian.com/software/jira
Requirements Engineering	Confluence	This is a wiki-based collaboration tool that can be used for requirements management	https://www.atlassian.com/software/confluence
Requirements Engineering	Visual Paradigm	This is a modelling tool that includes features for requirements engineering, such as the ability to create use cases, user stories, and requirements diagrams	https://www.visual-paradigm.com/
Requirements Engineering	Diagrams.net	Good general purpose Technical Diagram tool to help standardise, between partners, the format of architectural, process diagrams and layouts	https://app.diagrams.net/
ML Use cases Prioritisation	DataRobot	Automated machine learning platform that includes features for use case prioritisation. It uses a combination of artificial intelligence and human expertise to identify and prioritise potential use cases	https://www.datarobot.com/
ML Use cases Prioritisation	Microsoft Azure ML Studio	It includes tools for identifying and prioritising use cases	https://azure.microsoft.com/en-us/products/machine-learning/
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Phase II: Model Development

MLOps Step	Tool Name	Tool Description	Link
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Phase III: Operations

MLOps Step	Tool Name	Tool Description	Link
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Other Info

• Research by Software Competence Centre Hagenberg - SCCH