Integration and Production Application Guide

Welcome to the Integration and Production Application Guide repository! This guide is designed to help you integrate machine learning models into production applications using Roboflow as an API inference service. Whether you're a developer or a data scientist, this guide covers essential steps to seamlessly deploy and integrate models into your ASP.NET MVC projects.

Table of Contents

1. Pengenalan Roboflow sebagai API Inference
2. Akuisisi API Key
3. Penyiapan Project ASP.NET MVC
4. Penyiapan API dari Model yang Di-deploy ke Roboflow
5. Integrasi API dan Kode Inference pada ASP.NET MVC
6. Deployment ke IIS Server

1. Pengenalan Roboflow sebagai API Inference

Understand Roboflow's role as an API inference service for deploying machine learning models.

Resources:

• Roboflow API Documentation

2. Akuisisi API Key

Acquire the API key from Roboflow to authenticate and access the inference API.

Resources:

• Getting Your Roboflow API Key

3. Penyiapan Project ASP.NET MVC

Set up an ASP.NET MVC project as the foundation for integrating the machine learning model.

Resources:

• Create an ASP.NET MVC Project

4. Penyiapan API dari Model yang Di-deploy ke Roboflow

Prepare the API from the model you've deployed to Roboflow, ensuring it meets the requirements for inference.

Resources:

• Deploy a Model to Roboflow

5. Integrasi API dan Kode Inference pada ASP.NET MVC

Integrate the Roboflow API and inference code into your ASP.NET MVC project, allowing it to make predictions using the deployed model.

Resources:

• Integrating API in ASP.NET MVC

6. Deployment ke IIS Server

Deploy your ASP.NET MVC application, including the integrated machine learning model, to an IIS (Internet Information Services) server for production use.

Resources:

• Deploy an ASP.NET MVC Application to IIS

Feel free to explore each topic at your own pace. Happy integrating and deploying your machine learning models in production applications!