MOC Demo deployment scenarios using AZD.

Customers using R can run simulation and machine learning securely and at scale with Amazon SageMaker while also reducing the cost of development by using the fully elastic resources in the cloud. In this demo, learn how to build, train, and deploy statistical and ML models in R at scale using Amazon SageMaker from your IDE.

In this post, we will discuss how to implement an automatic pipeline to deploy your model trained with SageMaker to an AWS Lambda. You will find a demo regarding how to integrate these two services

Train, evaluate, deploy and demo a Custom CV Model for use with Meraki MV Cameras from scratch.

A demo project showing how to train a fastai2 model on an Amazon SageMaker notebook instance and deploy to SageMaker hosting services.

A machine learning web application that predicts the likelihood of credit card default using a Random Forest model trained on the UCI Credit Card dataset. Built with FastAPI for the backend and a clean HTML/CSS frontend,Fully containerized with Docker for easy deployment and demo-ready for showcasing model predictions.

Trainer-Demo-Deploy routing demo

Trainer-Demo-Deploy scenario for Azure Monitor including system metrics, custom logs, and external telemetry

Basic Demo for using Azure ML to Train and Deploy ML Models

Teach your robot anything from your browser. Teleoperate, record demos, train policies, deploy.

This demo shows how to train a model, track it with MLflow, and deploy it with MLflow in Azure.

GPT-2 built from scratch in PyTorch. Pre-trained on TinyStories with DDP on 4x H100s. Models on HuggingFace, live demo deployed.

This is a simple NodeJS / Express Application that uses SNS/DynamoDB. It is meant for technical trainer demos and is compatible with a variety of deployment methods, including AWS Elastic Beanstalk, AWS Proton, and more.

Deploy multiple neural network architectures quickly, view and record performance metrics, record trained weights for application deployment and/or to load into further training sessions later. Video demo in finance AI series on YouTube.

Practical MLOps template: Argo Workflows pipeline (prep → train → evaluate → register) tracked in MLflow, then deployed as a FastAPI service. Includes Dockerfiles, K8s manifests, HPA, Makefile, and CI. Demo: submit workflow, view MLflow run, call /predict.

Smart customer assistant demo showcasing multimodal AI: text/image/speech processing with intent classification & automated responses. Built with PyTorch, Transformers, OpenCV. Trained on synthetic data for demonstration; full production system deployed at scale in live environments.

Intelligent email analysis demo: spam detection, intent classification, summarization & criticality rating. Built with scikit-learn, Streamlit. Trained on synthetic data (5K emails). Production system deployed at company with real email data, Azure ML integration, 10K+ daily emails processed.

Railway Control is a full-stack railway operations dashboard for monitoring train movement, analyzing section conflicts, applying AI-driven control actions, and watching live simulation updates on a dynamic map. It includes seeded demo scenarios, authentication, dark and light themes, and Dockerized deployment

Agentic RAG system automating customer support with LangChain & FAISS vector search. 5 AI agents process queries in <1s with 85% accuracy. Demo trained on synthetic FAQs; production version deployed with real company data. Features: semantic search, multi-agent pipeline, interactive Streamlit UI. Python based.

basic demo to deploy ml model trained on iris dataset using flask

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Resnet train & deploy demo

IT5006 predictive policing demo deployment with trained models and Streamlit app

Deploy a openstack demo env of train version via kolla-ansbile step-by-step.

Simple demo of using a Sagemaker Studio notebook to train a model and deploy an API endpoint

ATLP elite demo -> This serves to train how to deploy a basic web app with cloud hosting platforms

Full-stack demo: trained ML model (scikit-learn) deployed as a Flask web app with HTML/CSS frontend.

A reall simple demo of a sentiment tf model trained in python and deployed to tfjs using gh pages

React.js web client demo app talking to a model trained and deployed on Coretex behind an API endpoint.

End to End Machine Learning Project, from model train and test to the UI to AWS Deployment - Demo Proj: Wine Quality Prediction