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

Transitioning from Backend Engineering to Machine Learning: A Technical Roadmap

Author(s) Snehansh Devera Konda
Country United States
Abstract A paradigm shift is taking place in the software business as machine learning becomes more and more essential to contemporary applications. This thorough technical roadmap offers a strategic framework for backend engineers looking to move into machine learning roles, addressing the increasing need for ML
professionals. It offers a systematic strategy that builds critical ML-specific competencies while utilizing current backend engineering expertise. The article provides actionable recommendations for professional advancement by thoroughly analyzing important transition components. It looks at basic topics such as the foundations of data engineering, which serve as the framework for machine learning systems; machine learning principles, which cover both theoretical and practical aspects; cloud platform integration for scalable solutions; containerization strategies for effective deployment; and mathematical prerequisites necessary to comprehend ML algorithms. In addition to outlining resource requirements and success criteria based on industry research, the article lays a strong emphasis on realistic implementation tactics.
By offering specific examples and best practices, it emphasizes the value of ongoing learning through portfolio construction and structured techniques. With insights into both technical and professional development issues, this article provides backend engineers with a thorough guide to managing the shift to machine learning.
Keywords Keywords: Machine Learning Transition, Data Engineering, Cloud-Based Platforms, Containerization, and Mathematical Foundation
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-20
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33266
Short DOI https://doi.org/g8w23b
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