MLOps: A Step Forward to Enterprise Machine Learning
Machine Learning Operations (MLOps) is becoming a highly crucial part of businesses looking to capitalize on the benefits of AI and ML models. This research presents a detailed review of MLOps, its benefits, difficulties, evolutions, and important underlying technologies such as MLOps frameworks, Docker, GitHub actions, and Kubernetes. The MLOps workflow, which includes model design, deployment, and operations, is explained in detail along with the various tools necessary for both model and data exploration and deployment. This article also puts light on the end-to-end production of ML projects using various maturity levels of automated pipelines, with the least at no automation at all and the highest with complete CI/CD and CT capabilities. Furthermore, a detailed example of an enterprise-level MLOps project for an object detection service is used to explain the workflow of the technology in a real-world scenario. For this purpose, a web application hosting a pre-trained model from TensorFlow 2 Model Zoo is packaged and deployed to the internet making sure that the system is scalable, reliable, and optimized for deployment at an enterprise level.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Using AntiPatterns to avoid MLOps Mistakes
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Ju…
Towards MLOps: A DevOps Tools Recommender System for Machine Learning System
Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection betw…
Recommendation SystemsMLOps: A Review
Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The signific…
Exploring MLOps Dynamics: An Experimental Analysis in a Real-World Machine Learning Project
This article presents an experiment focused on optimizing the MLOps (Machine Learning Operations) process, a crucial aspect of efficiently implementing machine learning projects. The objective is to identify patterns and…
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces challenges in terms of model functional…
Federated LearningManagement