A series of Terraform based recipes to provision popular MLOps stacks on the cloud.

Deploy production-grade Metaflow cloud infrastructure on AWS

Tools and utilities for operating Metaflow in production

A cloud-agnostic ML Platform that will enable Data Scientists to run multiple experiments, perform hyper parameter optimization, evaluate results and serve models (batch/realtime) while still maintaining a uniform development UX across cloud environments

Simply Automate Monitoring Infrastructure with Terraform, Ansible, AWS EC2, Nginx, Prometheus, Grafana and Github Actions 😄

Terraform frame to deploy a MLOps plattform on AWS EKS using Airflow, MLflow, and Jupyterhub.

Serverless MLOps pipeline for multi-account deployment with Step Functions and Terraform

Production-ready, secure ML infrastructure on AWS — zero-trust VPC, SageMaker domains, KMS, CodeArtifact, deployed with Terraform.

Code and artifacts from activity 2 of module 2 of ML Engineering (UFSCar postgraduate course). Project: ML model to classify Iris, Flask API for prediction, Docker, Terraform on AWS and CI/CD.

A collection of my personal DevOps projects, notes, and automation scripts. I share things I’ve learned about Terraform, Ansible, Kubernetes, Docker and different cloud providers. Whether you're just starting out or already deep into DevOps, feel free to explore, try things out, and maybe even contribute.

My Health App Infrastructure Repository

Benchmarking vLLM inference on Amazon EKS with GPU-accelerated serving, Kubernetes-native load generation, and Prometheus/Grafana observability.

Repository with exercises from Chapter 1 of Practical MLOps. Python application practices basic MLOps, using Makefile, GitHub Actions, Docker, AWS ECR, CodeBuild and Terraform.

A lightweight, cloud-native ML/AI experiment pipeline for GCP using Terraform, Vertex AI, and Kubeflow Pipelines. Automates infrastructure provisioning for experiment tracking, model training, and deployment with monitoring and CI/CD integration.

MLOps on Yolo Deployment

Projeto completo de MLOps (ponta a ponta) na cloud da AWS usando Terraform (infraestrutura como código) e deploy automático via CI/CD com GitHub Actions.

🚀 End-to-End MLOps Pipeline: Deploying a Sentiment Analysis FastAPI on AWS EKS using Terraform, Helm Charts, and Prometheus/Grafana. Features Auto-scaling (HPA), AWS ALB Ingress, and full Observability.

End-to-end scalable ML inference on EKS: KEDA-driven pod autoscaling with Prometheus custom metrics, Cluster Autoscaler for GPU node scaling, and NVIDIA GPU time-slicing to run multiple pods per GPU.

End-to-end serverless ML inference platform on AWS powered by SageMaker Serverless, Lambda, and Terraform. Production-ready architecture with secure IAM boundaries and global delivery.