The MLOps Workload Orchestrator solution helps you streamline and enforce architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services.

deploy ML Infrastructure and MLOps tooling anywhere quickly and with best practices with a single command

Practical guide to build end-to-end machine learning pipeline and deploy your model in production,

Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.

Guided accelerator consolidating best practice patterns, IaaC and AML code artefacts to provide a reference approach to implementing MLOps on Azure leveraging Azure ML.

PyTorch Lightning + Hydra. + Timm: A very user-friendly template for rapid and reproducible MLOps with best practices. ⚡🔥⚡

This is a capstone project associated with MLOps Zoomcamp. The end goal of the project is to build an end-to-end machine learning project containing feature engineering, training, validation, tracking, modeel deployment, hosting, and general engineering best practices aimed at making house price predictions.

The MLOps Playbook is designed to provide a centralized hub of curated resources for studying and mastering the principles, tools, and best practices of MLOps.

End-to-end platform for training, deploying, and monitoring a churn prediction model—built using MLOps best practices and tools applied from the DataTalksClub MLOps Zoomcamp. Project earned the highest-tier score (achieved by 11 out of 200+ cohort participants) in peer-reviewed project assessment.

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.

This repo showcases a project that transforms ML model training into a simplified, production-ready Kedro Dockerized Pipeline. It emphasizes best MLOps practices, enabling easy training, evaluation, and deployment of models, including XGBoost, LightGBM and Random Forest, with built-in visualization and logging features for effective monitoring.

A playground for building and serving Retrieval-Augmented Generation (RAG) systems using best practices in MLOps and LLMOps, with open-source tools.

Implementation of and end to end credit system scoring system. This starts from data ingestion to a deployment and monitoring with a full CICD with best practices of MLOPS.

Tutorial for deploying LLMs on Kubernetes following MLOps best practices with Helm, ArgoCD, and Istio.

Comprehensive MLOps project covering the end-to-end machine learning lifecycle. Includes tools, frameworks, and best practices for model development, deployment, monitoring, and automation.

A cloud-based Forex forecasting platform using machine learning to predict currency price movements in real time, built with strong software engineering and MLOps best practices for scalable data pipelines, APIs, and production deployment.

This is a repo for demonstrating mlops best practices

Standards to follow for achieving engineering excellence in ML and MLOps.

A standardized template for machine learning operations projects, providing structure and best practices for MLOps workflows.

Kaggle's Spaceship-Titanic Project with a Complete Machine Learning Lifecycle following MLOps Best Practices using Kedro and MLflow.

An end-to-end MLOps pipeline for house price prediction featuring automated training, deployment, and inference with best practices in software design patterns and machine learning operations.

Repository for the project of Machine Learning Operations course at DTU. The aim of the project is to put in practice the principles of MLOps and get familiarized with the best tools for it.

An end-to-end MLOps pipeline for a production-grade fraud detection model. This project demonstrates best practices including data versioning (DVC), experiment tracking (MLflow), CI/CD (GitHub Actions), containerization (Docker), deployment on GKE, and advanced model analysis (poisoning attacks, drift, fairness, explainability).

An Enterprise-Grade Machine Learning project scaffold for streamlining MLOps best practices.

Forecasting Vehicle Sales Using XGBoost – A Practical Guide to MLops Best Practices

Practical in depth hands-on MLOPs utilising best tools, practice and strategies

Churn prediction project using MLOps best practices

Implementing a RAG with best MLOPS practices

Implement data/ML pipeline with best MLOPS practices

AI/ML engineering insights | Data platforms | Cloud architecture | MLOps best practices