Introduction The process of deploying machine learning models is an important part of deploying AI technologies and
Machine Learning Mastery 1:12 pm on June 4, 2024
Deployment and management practices are crucial in ML model lifecycle for effective optimization, containerization, CI/CD implementation using Jenkins & Github Actions, performance monitoring with Kubernetes, alerting systems, and security measures.
- Model Optimization: Enhancing prediction accuracy and efficiency.
- Containerization and Orchestration: Ensuring model consistency across environments using tools like Docker, Kubernetes, and Helm.
- Continuous Integration & Deployment (CI/CD): Automating the workflow to deploy models rapidly and reliably with Jenkins & Github Actions.
- Model Performance Monitoring: Real-time tracking of model performance, resource usage, and response times using Kubernetes dashboard and alert systems.
Category(ies): Anthropic, Large Language Models
https://machinelearningmastery.com/tips-deploying-machine-learning-models-efficiently/
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