REVOLUTIONIZING MACHINE LEARNING MODEL SERVING WITH CONTAINERIZATION
Keywords:
Machine Learning,, Kubernetes, Containerization, CI/CD Integration, Security And IsolationAbstract
Machine learning (ML) is being used more and more in many fields, which has made it clear that we need deployment options that are both efficient and scalable. However, businesses have a hard time putting ML models to use and managing them in production environments. This article talks about how containerization technologies, like Docker and Kubernetes, can change the way ML models are served by solving important problems with deployment, scalability, collaboration, and security. By putting machine learning models inside containers and using Kubernetes for orchestration, businesses can get consistent and repeatable deployments, easy scaling, better communication between data scientists and DevOps teams, and higher security through isolation and detailed access control. Real-life case studies and industry polls show that containerization in ML model serving has real benefits, such as shorter deployment times, better compliance, and better resource utilization. As containerization is used more and more in machine learning, it's clear that this method will be very important for helping companies get the most out of their ML projects.
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Copyright (c) 2024 Sailesh Oduri (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
