mlops-template

Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.

Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

GenAIOps with Prompt Flow is a "GenAIOps template and guidance" to help you build LLM-infused apps using Prompt Flow. It offers a range of features including Centralized Code Hosting, Lifecycle Management, Variant and Hyperparameter Experimentation, A/B Deployment, reporting for all runs and experiments and so on.

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana.

A project to kickstart your ML development

🍪 Cookiecutter template for MLOps Project. Based on: https://mlops-guide.github.io/

End-to-end ML project for tabular data.

A CLI & UI tool that generates production-ready MLOps project templates for Scikit-learn, PyTorch, and TensorFlow.

Using MLflow to deploy your RAG pipeline, using LLamaIndex, Langchain and Ollama/HuggingfaceLLMs/Groq

A starter template for machine learning projects as a tutorial base

An AI-powered cloud threat detection system with a full MLOps lifecycle multi-source log ingestion, unsupervised anomaly detection, MITRE ATT&CK mapping, CVE enrichment, and a Claude-powered SOC analyst, all wired into a Kubeflow pipeline that trains, gates, and deploys to KServe automatically.

Here are some common use cases and experiments based on tutorials found at Machine Learning Mastery for the libmlops library.

MLOps talks - joint effort with Amsterdam MLOps.community

ETL Pipeline best practices implemented in this repository. Also check out the experiments tab on Dagshub

A FastAPI service that predicts restaurant tips using a machine learning model. Deployed on AWS EC2 with Docker and CI/CD via GitHub Actions.

An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana.

GCP ML Framework