International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

MLOps and DataOps Integration for Scalable Machine Learning Deployment

Author(s) Bhanu Prakash Reddy Rella
Country United States
Abstract The quick market expansion of ML has led industries to create robust operational systems for managing the deployment, monitoring, and scalability of ML models. Through MLOps (Machine Learning Operations) and DataOps (Data Operations), businesses gain efficient ML deployment and model development capabilities that automate and enhance operations for model training, deployment, and data control. MLOps handles ML model lifecycle management, yet DataOps maintains both data pipeline quality standards and reliability, which makes them vital elements for developing ML systems ready for production use.
The inability to create a unified integration between MLOps and DataOps produces various operational challenges that cause data inconsistencies and model drifts, and decrease operational efficiency levels that limit large-scale machine learning deployments. The study examines how MLOps and DataOps cooperate to solve essential problems, including data management, streamlining automation pipelines, and operational visibility maintenance. The paper introduces automated CI/CD for ML combined with feature store implementation and real-time observability as integration approaches which enhance reproducibility and scalability and improve model performance.
Organizations that connect DataOps with MLOps achieve faster model delivery timelines and enhance data quality control and automated model improvement processes. This document investigates actual project examples that prove integrated MLOps-DataOps workflows successfully work in sectors such as finance, healthcare, and e-commerce.
Keywords AI/ML Pipeline Optimization, Data Science Workflow Integration, DataOps, Machine Learning Deployment, Model Deployment Automation, MLOps, Scalable AI
Published In Volume 4, Issue 1, January-February 2022
Published On 2022-01-05
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com