MLOps & Cloud Deployment

I will build scalable MLOps workflows and deploy your models in cloud environments with proper CI/CD, monitoring, and versioning.

Tech Stack:

    • Cloud: AWS (S3, Lambda, EC2), Azure ML, GCP AI Platform

    • Pipelines: MLflow, DVC, Airflow, Prefect

    • Containers: Docker, Kubernetes, Helm

    • APIs: FastAPI, Flask

    • CI/CD: GitHub Actions, GitLab CI, Terraform

    • Monitoring: Prometheus, Grafana, ELK stack

    • Model Registry: MLflow, SageMaker

Deliverables:

    • End-to-end ML pipeline (data → train → deploy)

    • Model registry + artifact tracking

    • Dockerized model + infrastructure-as-code

    • API deployment to cloud instance

    • Real-time metrics & logging

    • Optional GPU setup or autoscaling configuration