End-to-End MLOps Deployment Using GitLab CI | ArgoCd | Kubernetes |

In this video, I walk you through the entire process of deploying a Machine Learning Operations (MLOps) pipeline using GitLab CI/CD. You'll learn how to set up a robust workflow that automates data preprocessing, model training, and deployment.
What You'll Learn:
Overview of MLOps and its importance
Step-by-step guide to creating a GitLab CI/CD pipeline
How to integrate Docker for containerization and Deploying into Kubetrnetes.
Best practices for version control in machine learning projects
Tips for monitoring and maintaining your deployed models
Whether you're a beginner or looking to enhance your MLOps skills, this video will provide you with the knowledge and tools to successfully deploy machine learning models.
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Links:
Gitlab Runner configuration:
www.geeksforge...
GitLab Repository: gitlab.com/mlops6343834/mlops-flask
ArgoCd installation commands:
1) kubectl create namespace argocd
2) kubectl apply -n argocd -f raw.githubuser...
3) kubectl expose service argocd-server --type=LoadBalancer --name=argocd-server -n argocd
4) kubectl get secret argocd-initial-admin-secret -n argocd -o jsonpath='{.data.password}' | base64 --decode (get the argocd password)
Docker image registry: hub.docker.com...
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