MLOps & Deployment
MLOps
DevOps Engineer
Project Overview
Managing the end-to-end machine learning lifecycle, from building CI/CD pipelines with Azure DevOps and Azure ML Space, containerizing models with Docker for scalable deployment.
Detailed Description
Comprehensive skills in managing the complete machine learning lifecycle (MLOps).
- Designing and implementing robust CI/CD pipelines using Azure DevOps and Jenkins.
- Containerizing models with Docker for portability.
- Orchestrating scalable deployments with Kubernetes to ensure high availability and performance.
Key Technologies
Python
Azure DevOps
Azure ML Space
Docker
Kubernetes
Challenges & Solutions
Automating the model retraining and deployment process while ensuring zero downtime was a significant challenge, requiring the implementation of blue-green deployment strategies for ML models.