A GitHub Action for deploying a Prefect flow to Prefect Cloud

The Zoomcamp MLOps Course covers tools like MLflow, Mage, Flask, Prometheus, Evidently, Grafana, Prefect, Terraform, and GitHub Actions. It emphasizes experiment tracking, model deployment, monitoring, CI/CD, and orchestration, culminating in an end-to-end project integrating best practices in MLOps.

Training and deploying XGBoost Model with MLFlow, Fastapi, Prefect, Cloud Run and github actions

This project sets up a full-stack MLOps pipeline, integrating GCP, BigQuery, Terraform, MLflow, GitHub Actions, Docker, Cloud Run, FastAPI, Prefect, and dbt, covering everything from infrastructure to deployment.

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Automate deployment registration with Prefect Cloud using GitHub Actions.

Repo with the details for 11/7 Prefect stream about using Github Actions to deploy your Prefect flows

Working examples of dlt data pipelines deployed with github actions, Airflow, Dagster, Prefect, etc

Codes and notes on Productionizing Machine Learning Services: experiment tracking (Mlflow), orchestration(Mage, Prefect), deployment(AWS kinesis, lambda, flask ), monitoring (Grafana, Evidently, Prefect), containerize (docker, K8S), CI/CD (Github actions)

MLOps project for predicting hotel booking cancellations. The project covers the full ML lifecycle: data ingestion, preprocessing, model training with MLflow tracking & registry, batch inference, data-drift monitoring with Evidently, orchestration with Prefect, containerization with Docker, CI/CD with GitHub Actions and AWS deployment.