Continuous Deployment of ML Model with Jenkins, Kubeflow and Kubernetes | MLOps скачать в хорошем качестве

Continuous Deployment of ML Model with Jenkins, Kubeflow and Kubernetes | MLOps

In this video, we take a deep dive into setting up a Continuous Deployment (CD) pipeline for machine learning models using Jenkins, Kubeflow, and Kubernetes. Learn how to automate the deployment of ML models from training to production, ensuring faster and more reliable model updates. What You'll Learn: How to set up a CI/CD pipeline for ML workflows Integrating Jenkins with Kubeflow Pipelines Deploying ML models to a Kubernetes cluster Automating retraining and redeployment MLOps best practices for scalability and reliability 💡 Whether you're a DevOps engineer, MLOps enthusiast, or data scientist looking to streamline your ML deployment workflows, this video is for you. 📌 Tools Used: Jenkins Kubeflow Kubernetes Docker GitHub AWS S3 #mlops | #kubernetes | #kubeflow | #jenkins | #ml | #devops