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 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

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

Advancements in Cloud-Based Machine Learning: Navigating Deployment and Scalability

Author(s) Mayank Jindal
Country United States
Abstract The widespread adoption of machine learning (ML) in various industries has brought to light significant challenges, particularly in deploying these complex models into production environments. The need for scalable, efficient, and robust solutions is paramount, and cloud computing emerges as a key player in this scenario. Cloud platforms offer the necessary infrastructure and tools to facilitate ML deployment, addressing issues like computational demand, data storage, and scalability. Within the cloud computing landscape, AWS SageMaker, a service provided by Amazon Web Services, has gained prominence. This paper undertakes a comprehensive review of the machine learning (ML) lifecycle within cloud-based platforms with a specific focus on AWS SageMaker. Additionally, this paper explores the critical aspect of scaling in ML deployment, analyzing both horizontal and vertical scaling methods within the context of cloud computing's dynamic resource management. This paper aims to deliver an in-depth analysis of the ML lifecycle in cloud platforms by elucidating the functionalities, benefits, and challenges of using AWS SageMaker in the broader spectrum of ML deployment and management.
Keywords Machine Learning Deployment, Cloud Computing, Scalability
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-30
Cite This Advancements in Cloud-Based Machine Learning: Navigating Deployment and Scalability - Mayank Jindal - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.11371
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.11371
Short DOI https://doi.org/gtbtdz
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 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

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 +91-9898-5948-55 editor@ijfmr.com